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We've been talking about DevOps a lot on the Packt Podcast. The reason for that is simple: it's a critical part of how we actually build software from both a technical and an organizational perspective. And anything that draws us closer to the relationship between people and software can only be a good thing right? For this edition of the Packt Podcast we spoke to Nigel Kersten, who's the VP of Ecosystem Engineering at Puppet. With Puppet playing an important role in the evolution of DevOps over the last decade or so, we thought he would be a great person to give an insight not only on how Puppet has been adapting to industry trends (yes, we're waving at you, Kubernetes). Listen to the episode: https://soundcloud.com/packt-podcasts/puppets-vp-of-engineering-nigel-kersten-on-the-organizational-challenges-of-devops Nigel Kersten talks DevOps We covered a diverse range of topics in the episode. From Nigel's move from Google to Puppet (which, he tells us, slightly upset his mom...), through to the challenges - and pitfalls - engineering teams face when trying to implement DevOps. Read next: DevOps engineering and full-stack development – 2 sides of the same agile coin Key quotes from this podcast episode How to automate workflows effectively “One thing we definitely tell people to do is… don’t automate one service from end to end. Don’t pick one complicated three tier web application put a small team on it and say “your job is to puppetize all of this infrastructure. What, instead, is a more powerful way to work is you go what are those low level building blocks that are across all of your infrastructure...? What are the things that are common across all of your infrastructure? Automate those things because they’re often really simple to do, and the rewards are huge.”  “Look at the things that are causing you pain in production. If you go and talk to the people who are on call, in charge of deployments, any of those parts of your infrastructure and ask them what would be the one thing that you would fix that would make your infrastructure more reliable, they will always have a shortlist of things… and when you do this, you start building trust across the whole organization.” The fear of automation “There’s always fear about adopting automation. There’s always fear about people’s jobs changing and adopting new tools and disciplines - sort of in an endless cycle of new tool adoption, people being told that they have to learn new things - the more you can actually show value across the whole organization that this thing’s relatively easy, a small investment for large returns, the more powerful an effect you're actually going to have.” DevOps challenges “I think it’s a huge mistake if people think they’re embarking on a DevOps journey and they’re not willing to actually make some of the cultural and organizational changes - it’s about creating more cross-functional teams, it’s about giving them more autonomy, and it’s about actually letting people work across organizational boundaries without having to go up and down the hierarchy of the organization.” “Most people are actually struggling pre-DevOps in many ways… the people who we’ve seen fail are the ones who have gone, look we’re going to jump exactly from where we are now and try to move to an incredibly automated environment without putting a lot of the ground work in place  - like building up trust within the org, giving teams more autonomy, allowing service owners to configure monitoring themselves - I think all of those sorts of things are really prerequisites for a whole organization succeeding at DevOps.”

Last month there was a huge furore around FaceApp, the mobile application that ages your photographs to show you what you might look like as you get older. This was caused by a rapid cycle of misinformation and conjecture. It was thanks to cybersecurity researcher Elliot Alderson - who you might remember from last week's podcast episode - that the world was able to get beyond speculation and find out what was really going on. We got in touch with Elliot shortly after the story broke. He was kind enough to speak to us about the FaceApp furore, and explained what caused the confusion and how he managed to get to the bottom of what was actually going on. You can listen to what he had to say in this special short bonus episode: https://soundcloud.com/packt-podcasts/bonus-security-researcher-elliot-alderson-on-the-faceapp-furore   Elliot says that although FaceApp is problematic, it isn't unique. It poses exactly the same threat to our privacy as the platforms and applications that millions of people use every day. "There is an issue with FaceApp, he tells us. "But there is an issue with Facebook, with SnapChat, with Twitter - it's never a good idea for someone to upload a photo of your face to a random application." This line of argument can be found elsewhere. Arguably the most important lesson we can learn. In this article from Wired, journalist Brian Barrett writes "should you be worried about FaceApp? Sure. But not necessarily more than any other app you let into your photo library." Should you use FaceApp? However, although you might assume that a security professional would simply warn everyone against using these sorts of applications, Elliot says "this application is really trendy. You can see a lot of stars using it on social media, so this is normal - you want to use this application." What you need to consider if want to use FaceApp However, if you do want to use it, you should be careful. "You have to step back a little bit before using it and ask yourself a question" about how money is being made. "this is a free application... there are developers behind this application, they need to live, they need to eat, they need to live, they need to eat - they need to earn money - and in general the answer is with your data." "You are the information." Elliot says. "You can decide to use it, and say okay, I'm ready to lose this part of my privacy in order to use this cool service... or you will... think no, it's not worth it. FaceApp seems to be cool, but my privacy is more important than something trendy like this." The key, then, is to check the terms and conditions of the application. "You have to know that you will have lost a part of your privacy, And if you're okay with that then - okay, go for it, and use the application." "Developers need to say no sometimes." Developer responsibility and code ethics There are clearly question marks for users about FaceApp, or, indeed, any other free application that has access to your data. But what about the developers building these applications? Do they have a part to play in ensuring that applications respect user consent and privacy? "It's complicated for a developer to say no to their project manager" says Elliot. However, this doesn't mean developers should be content to follow orders from management. "Developers need to raise their level... and say okay, but ethics is also important..." Elliot continues, "as a technical guy I need to spread the message internally in my company, and say to the project manager, to the business, to the marketing department okay this is a cool feature but no, we won't do that because this is against our user'." "Developers need to say no sometimes - and companies need to understand that it's not okay to dump as much data as possible from their users." How did Elliot Alderson uncover the truth about FaceApp? One thing that is often forgotten in these stories are the technical processes through which the truth is uncovered. Sure, that might be a little dry or complicated for some, but the fact that there is real detective work in understanding what's actually going on inside an application is incredibly interesting. It also highlights that while software might sometimes appear mysterious or even impenetrable, with the right skills and tools we can see how things actually work. That's not only useful from a technical perspective, it's also a way for all of us to retrieve a small sense of power back from applications built and owned by companies worth billions of dollars. "It's not that easy, but it's not super complicated too," says Elliot. Although he tells us that "the first time you want to do it you need to spend some time on it for sure," once you're set up and ready to go you can find things out remarkably fast. Using a tool called Burp Suite, the whole process was complete in a matter of moments. "Checking FaceApp took literally 5 minutes for me, because everything is already set up on my computer and I just have to install the application and look at the network request." Learn more about Burp Suite with Packt's selection of eBooks and videos here. Follow Elliot Alderson on Twitter: @fs0c131y

Software security has been 'shifting left' in recent years. Thanks to movements like Agile and Dev(Sec)Ops, software developers are finding that they have to take more responsibility for the security of their code. By moving performance and security testing earlier in the development lifecycle it's much easier to identify and capture defects and issues. The reasons for this are largely rooted in the utter dominance of open source software and the increasingly distributed nature of the systems we're building. To put it bluntly, if our software is open, and loosely connected, the opportunity for systems to be exploited by malignant actors grows vastly. To tackle this we're starting to see a wealth of platforms and tools emerge that are trying to support developers embrace security as a fundamental part of the development process. One such platform is Semmle, a code analysis platform designed to help developers and engineers identify issues quickly. To find out more about Semmle - and the wider DevSecOps movement - we spoke to Chief Security Officer Fermin Serna in an edition of the Packt Podcast. He explained how Semmle works, what its trying to achieve, and placed it in the broader context of this 'shift left' that's quickly becoming a new reality for many engineers. Listen to the episode: https://soundcloud.com/packt-podcasts/we-need-to-democratize-security-how-semmle-is-improving-open-source-security   To learn more about Semmle, visit its website here. You can also follow Fermin Serna on Twitter: @fjserna. Read next:  5 reasons poor communication can sink DevSecOps How Chaos Engineering can help predict and prevent cyber-attacks preemptively

Quickbooks, the accounting software package developed and marketed by Intuit is targeted towards small and medium-sized businesses. It offers on-premises accounting applications and cloud-based versions that can undertake remote access capabilities like remote payroll assistance and outsourcing, electronic payment functions, online banking, and reconciliation, mapping features, and more. To know more about Quickbooks’ latest features and its learning curve for beginners, we did a quick interview with Crystalynn Shelton, a certified QuickBooks ProAdvisor and author of the book ‘Mastering QuickBooks 2020’. With more than 10 years of experience in Quickbooks, Shelton says Quickbooks is not only user-friendly but also cost-effective. Further, when asked about her views on QuickBooks online, Shelton points out that its live unlimited technical support is one of its main features  On Quickbooks, its benefits and use cases What are some of the advantages of Quickbooks that sets it apart from its competitors?  QuickBooks has a number of advantages that set them apart from its competitors. First, it is affordable for most small businesses. Whether you purchase an Online subscription (starting at $20/month) or a desktop product (starting at a one-time fee of $199), there is something for every budget. Another benefit of using QuickBooks is the program is very user-friendly. Most small business owners purchase the software and are able to set it up without having an IT person on staff.  In addition, there are a number of training videos, an extensive help menu within the program not to mention live tech support if you need it. Because QuickBooks is the most widely used accounting software program used by small businesses, most accountants and CPAs are familiar with the program. Some of these folks are certified ProAdvisors (like myself). They can offer consulting, training, and even bookkeeping services to small business owners who use QuickBooks. Can you elaborate on how small businesses can take benefit from Quickbooks? Also, how does Quickbooks simplifies tasks for them?  While there are numerous reasons why small businesses decide to use QuickBooks, there are five that tend to be the most common reasons: small businesses who can’t afford to hire a bookkeeper, small businesses who have outgrown the use of Excel spreadsheets and need a more sophisticated way to track income and expenses, small businesses who need financial statements in order to apply for a line of credit or business loan, small businesses whose tax professional will no longer accept a shoebox of receipts to file taxes. QuickBooks simplifies bookkeeping by allowing you to track all aspects of the business in one place: accounts payable, accounts receivable, income, and expenses. It uses simple language such as “people who owe you” (aka accounts receivable) or “what you owe to others” (aka accounts payable) to help business owners without prior bookkeeping knowledge comprehend the program. QuickBooks allows you to accept credit card payments from customers so you can get paid faster and easily reconcile payments to open invoices. Not to mention you can reduce (if not eliminate) manual data entry by connecting all of your business bank and credit card accounts to QBO.  Can you elaborate on how your book ‘Mastering QuickBooks 2020’ will prepare bookkeepers and accounting students in learning the ropes of QuickBooks? Also, how does the learning curve look like for users who have no bookkeeping knowledge and no experience with QuickBooks? This book was written with the assumption that the reader has no experience or knowledge of bookkeeping. We use simple language to explain how QuickBooks works and we have also provided screenshots to support the concepts being taught.  Chapter 1 includes a section that covers bookkeeping basics which will help non-accountants gain a better understanding of the terminology used in the field of accounting as well as QuickBooks. This information will help aspiring accountants build on their existing bookkeeping knowledge.  In addition, we have included the behind the scenes debits and credits for certain transactions to help accounting students prepare for the CPA exam or other academic tests. Shelton’s views on QuickBooks Online and Desktop What are your thoughts on QuickBooks Online and Quickbooks Desktop? What are the benefits of cloud accounting over Desktop? Do factors such as the size of an organization, or its maturity matter in choosing between the online and the desktop version? There are several benefits of using cloud accounting software over the desktop. Cloud accounting software allows you to manage your business from any device with an internet connection; whereas desktop limits you to a desktop computer. With cloud accounting software like QuickBooks Online, you can give anyone access to your QuickBooks data without them having to travel to your office. Cloud accounting software includes automatic real-time updates of your data. Unlike desktop software, you don’t have to worry about backing up your data with Online; its automatically done for you. Finally, QuickBooks Online includes unlimited live technical support. This is an invaluable feature for small business owners who are managing their own books and need the ability to get help when they need it. The size of an organization, structure, and length of time in business can definitely impact whether a business should choose QuickBooks Online or desktop. As a QuickBooks ProAdvisor, one of the first things I do is conduct an assessment to determine what the needs of my clients are. This involves documenting the details of their current processes (i.e. invoicing customers, paying bills, managing inventory, etc.)  Once I have this information, I am able to determine whether QuickBooks desktop is right or if QuickBooks Online is the best fit. If both products are ideal, I provide my clients with the downsides (if any) of going with one product over the other. This gives my clients all of the information they need to make an informed decision. On how Quickbook secures online data How does Quickbooks help in securing payments? How does QuickBooks keep online data safe? To secure payments, intuit transmits, support, protect, and access all cardholder information in compliance with the Payment Card Industry’s (PCI) data security standards. Additional security precautions Intuit has implemented are as follows: All data between Intuit servers and their customers is encrypted with at least 128-bit TLS, and all copies of daily backup data are encrypted with 256-bit AES encryption. Data is kept secure with multiple servers housed in Tier-3 data centers that have strict access controls and real-time video monitoring of the data center. All servers are hardened Linux installations, which are monitored in real-time and kept up-to-date with security patches. Can you suggest some best practices (at least five) that will help Quickbook aspirants in saving time and becoming a Quickbook pro? There are several ways you can save time and become proficient in QuickBooks Online. First, I recommend that you use QuickBooks on a daily basis. The more hands-on experience with QuickBooks, the more proficient you will become. Second, take the time to properly set up your QuickBooks account before you start entering transactions.  In Chapter 2, we provide you with a detailed checklist which includes what information you need to setup QuickBooks. By taking the time to set up customers, vendors, the chart of accounts, and your products and services upfront, the less time you will spend having to do it later on when you are trying to enter data. Third, all aspiring bookkeepers and accountants should get certified in QuickBooks Online. Certification is offered through Intuit and it is free.  As a Certified QuickBooks ProAdvisor, you get access to product discounts, marketing materials to promote bookkeeping services to prospective clients, a certification badge and designation you can put on business cards, websites and email signature lines.  Fourth, utilize keyboard shortcuts. They will save you time as you navigate the program. We have included a list of QBO keyboard shortcuts in the appendix of this book. Finally, connect as many bank and credit card accounts as you can to QBO. By doing so, you will reduce the amount of manual data entry required which will help you to keep your books up-to-date. If you want to learn how to build the perfect budget, simplify tax return preparation, manage inventory, track job costs, generate income statements and financial reports, check out Crystalynn’s book ‘Mastering QuickBooks 2020’. This book will work for a small business owner, bookkeeper, or accounting student who wants to learn how to make the most of QuickBooks Online. About the author Crystalynn Shelton is a licensed Certified Public Accountant, a certified QuickBooks ProAdvisor and has been certified in QuickBooks for more than 10 years. Crystalynn is currently a staff writer for Fit Small Business and an Adjunct Instructor at UCLA Extension where she teaches accounting, bookkeeping and QuickBooks to hundreds of small business owners and accounting students each year. Her previous experience includes working at Intuit (QuickBooks) as a Sr. Learning Specialist.  MongoDB’s CTO Eliot Horowitz on what’s new in MongoDB 4.2, Ops Manager, Atlas, and more New QGIS 3D capabilities and future plans presented by Martin Dobias, a core QGIS developer Greg Walters on PyTorch and real-world implementations and future potential of GANs Elastic marks its entry in security analytics market with Elastic SIEM and Endgame acquisition “The challenge in Deep Learning is to sustain the current pace of innovation”, explains Ivan Vasilev, machine learning engineer

Introduced in 2014, GANs (Generative Adversarial Networks) was first presented by Ian Goodfellow and other researchers at the University of Montreal. It comprises of two deep networks, the generator which generates data instances, and the discriminator which evaluates the data for authenticity. GANs works not only as a form of generative model for unsupervised learning, but also has proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. In this article, we are in conversation with Greg Walters, one of the authors of the book 'Hands-On Generative Adversarial Networks with PyTorch 1.x', where we discuss some of the real-world applications of GANs. According to Greg, facial recognition and age progression will one of the areas where GANs will shine in the future. He believes that with time GANs will soon be visible in more real-world applications, as with GANs the possibilities are unlimited. On why PyTorch for building GANs Why choose PyTorch for GANs? Is PyTorch better than other popular frameworks like Tensorflow? Both PyTorch and Tensorflow are good products. Tensorflow is based on code from Google and PyTorch is based on code from Facebook. I think that PyTorch is more pythonic and (in my opinion) is easier to learn. Tensorflow is two years older than PyTorch, which gives it a bit of an edge, and does have a few advantages over PyTorch like visualization and deploying trained models to the web. However, one of the biggest advantages that PyTorch has is the ability to handle distributed training. It’s much easier when using PyTorch. I’m sure that both groups are looking at trying to lessen the gaps that exist and that we will see big changes in both. Refer to Chapter 4 of my book to learn how to use PyTorch to train a GAN model. Have you had a chance to explore the recently released PyTorch 1.3 version? What are your thoughts on the experimental feature - named tensors? How do you think it will help developers in getting a more readable and maintainable code? What are your thoughts on other features like PyTorch Mobile and 8-bit model quantization for mobile-optimized AI? The book was originally written to introduce PyTorch 1.0 but quickly evolved to work with PyTorch 1.3.x. Things are moving very quickly for PyTorch, so it presents an evermoving target.  Named tensors are very exciting to me. I haven’t had a chance to spend a tremendous amount of time on them yet, but I plan to continue working with them and explore them deeply. I believe that they will help make some of the concepts of manipulating tensors much easier for beginners to understand and read and understand the code created by others. This will help create more novel and useful GANs for the future. The same can be said for PyTorch Mobile. Expanding capabilities to more (and less expensive) processor types like ARM creates more opportunities for programmers and companies that don’t have the high-end capabilities. Consider the possibilities of running a heavy-duty AI on a $35 Raspberry Pi. The possibilities are endless. With PyTorch Mobile, both Android and iOS devices can benefit from the new advances in image recognition and other AI programs. The 8-bit model quantization allows tensor operations to be done using integers rather than floating-point values, allowing models to be more compact. I can’t begin to speculate on what this will bring us in the way of applications in the future. You can read Chapter 2 of my book to know more about the new features in PyTorch 1.3. On challenges and real-world applications of GANs GANs have found some very interesting implementations in the past year like a deepfake that can animate your face with just your voice, a neural GAN to fight fake news, a CycleGAN to visualize the effects of climate change, and more. Most of the GAN implementations are built for experimentation or research purposes. Do you think GANs can soon translate to solve real-world problems? What do you think are the current challenge that restrict GANs from being implemented in real-world scenarios? Yes. I do believe that we will see GANs starting to move to more real-world applications. Remember that in the grand scheme of things, GANs are still fairly new. 2014 wasn’t that long ago. We will see things start to pop in 2020 and move forward from there. As to the current challenges, I think that it’s simply a matter of getting the word out. Many people who are conversant with Machine Learning still haven’t heard of GANs, mainly due to the fact that they are so busy with what they know and are comfortable with, so they haven’t had the time and/or energy to explore GANs yet. That will change. Of course, things change on almost a daily basis, so who can guess where we will be in another two years? Some of the existing and future applications that GANs can help implement include new photo-realistic scenes for video games, movies, and television, taking sketches from designers and making realistic photographs in both the fashion industry and architecture, taking a partial facial image and making a rotated view for better facial recognition, age progression and regression and so much more. Pretty much anything with a pattern, be it image or text can be manipulated using GANs. There are a variety of GANs available out there. How should one approach them in terms of problem solving? What are the other possible ways to group GANs? That’s a very hard question to answer. You are correct, there are a large number of GANs in “the wild” and some work better for some things than others. That was one of the big challenges of writing the book.  Add to that, new GANs are coming out all the time that continue to get better and better and extend the possibility matrix. The best suggestion that I could make here is to use the resources of the Internet and read, read and read. Try one or two to see what works best for your application. Also, create your own category list that you create based on your research. Continue to refine the categories as you go. Then share your findings so others can benefit from what you’ve learned. New GANs implementations and future potential In your book, 'Hands-On Generative Adversarial Networks with PyTorch 1.x', you have demonstrated how GANs can be used in image restoration problems, such as super-resolution image reconstruction and image inpainting. How do SRGAN help in improving the resolution of images and performing image inpainting? What other deep learning models can be used to address image restoration problems? What are other keep image related problems where GANs are useful and relevant? Well, that is sort of like asking “how long is a piece of string”. Picture a painting in a museum that has been damaged from fire or over time. Right now, we have to rely on very highly trained experts who spend hundreds of hours to bring the painting back to its original glory. However, it’s still an approximation of what the expert THINKS the original was to be. With things like SRGAN, we can see old photos “restored” to what they were originally. We already can see colorized versions of some black and white classic films and television shows. The possibilities are endless. Image restoration is not limited to GANs, but at the moment seems to be one of the most widely used methods. Fairly new methods like ARGAN (Artifact Reduction GAN) and FD-GAN (Face De-Morphing GAN or Feature Distilling GAN) are showing a lot of promise. By the time I’m finished with this interview, there could be three or more others that will surpass these.  ARGAN is similar and can work with SRGAN to aid in image reconstruction. FD-GAN can be used to work with human position images, creating different poses from a totally different pose. This has any number of possibilities from simple fashion shots too, again, photo-realistic images for games, movies and television shows. Find more about image restoration from Chapter 7 of my book. GANs are labeled as innovative due to its ability to generate fake data that looks real. The latest developments in GANs allows it to generate high-dimensional fake data or image video that can easily go undetected. What is your take on the ethical issues surrounding GANs? Don’t you think developers should target creating GANs that will be good for humanity rather than developing scary AI capabilities? Good question. However, the same question has been asked about almost every advance in technology since rainbows were in black and white. Take, for example, the discussion in Chapter 6 where we use CycleGAN to create van Gogh like images. As I was running the code we present, I was constantly amazed by how well the Generator kept coming up with better fakes that looked more and more like they were done by the Master. Yes, there is always the potential for using the technology for “wrong” purposes. That has always been the case. We already have AI that can create images that can fool talent scouts and fake news stories. J. Hector Fezandie said back in 1894, "with great power comes great responsibility" and was repeated by Peter Parker’s Uncle Ben thanks to Stan Lee. It was very true then and is still just as true. How do you think GANs will be contributing to AI innovations in the future? Are you expecting/excited to see an implementation of GANs in a particular area/domain in the coming years? 5 years ago, GANs were pretty much unknown and were only in the very early stages of reality.  At that point, no one knew the multitude of directions that GANs would head towards. I can’t begin to imagine where GANs will take us in the next two years, much let the far future. I can’t imagine any area that wouldn’t benefit from the use of GANs. One of the subjects we wanted to cover was facial recognition and age progression, but we couldn’t get permission to use the dataset. It’s a shame, but that will be one of the areas that GANs will shine in for the future. Things like biomedical research could be one area that might really be helped by GANs. I hate to keep using this phrase, but the possibilities are unlimited. If you want to learn how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems, read Greg’s book ‘Hands-On Generative Adversarial Networks with PyTorch 1.x’. This book highlights all the key improvements in GANs over generative models and will help guide you to make the GANs with the help of hands-on examples. What are generative adversarial networks (GANs) and how do they work? [Video] Generative Adversarial Networks: Generate images using Keras GAN [Tutorial] What you need to know about Generative Adversarial Networks ICLR 2019 Highlights: Algorithmic fairness, AI for social good, climate change, protein structures, GAN magic, adversarial ML and much more Interpretation of Functional APIs in Deep Neural Networks by Rowel Atienza

How will web and app development evolve in 2019? What are some of the key technologies that you should be investigating if you want to stay up to date in the new year? And what can give you a competitive advantage? This post should help you get the lowdown on some of the shifting trends to be aware of, but I also sat down to discuss some of these issues with my colleague Stacy in the second Packt podcast. https://soundcloud.com/packt-podcasts/why-the-stack-will-continue-to-shrink-for-app-and-web-developers-in-2019 Let us know what you think - and if there's anything you'd like us to discuss on future podcasts, please get in touch!

The Industrial Internet, the IIoT, the 4th Industrial Revolution or Industry 4.0, whatever you may call it, has gained a lot of traction in recent times. Many leading companies are driving this revolution, connecting smart edge devices to cloud-based analysis platforms and solving their business challenges in new and smarter ways. To ensure the smooth integration of such machines and devices, effective architectural strategies based on accepted principles, best practices, and lessons learned, must be applied. In this interview, Shyam throws light on his new book, Architecting the Industrial Internet, and shares expert insights into the world of IIoT, Big Data, Artificial Intelligence and more. Shyam Nath Shyam is the director of technology integrations for Industrial IoT at GE Digital. His area of focus is building go-to-market solutions. His technical expertise lies in big data and analytics architecture and solutions with focus on IoT. He joined GE in Sep 2013 prior to which he has worked in IBM, Deloitte, Oracle, and Halliburton. He is the Founder/President of the BIWA Group, a global community of professional in Big Data, analytics, and IoT. He has often been listed as one of the top social media influencers for Industrial IoT. You can follow him on Twitter @ShyamVaran.   He talks about the IIoT, the various impacts that technologies like AI and Deep Learning will have on IIoT and he gives a futuristic direction to where IIoT is headed towards. He talks about the challenges that Architects face while architecting IIoT solutions and how his book will help them overcome such issues. Key Takeaways The fourth Industrial Revolution will break silos and bring IT and Ops teams together to function more smoothly. Choosing the right technology to work with involves taking risks and experimenting with custom solutions. The Predix platform and Predix.io allow developers and architects, quickly learn from others and build working prototypes that can be used to get quick feedback from the business users. Interoperability issues and a lack of understanding of all the security ramifications of the hyper-connected world could be a few challenges that adoption of IIoT must overcome Supporting technologies like AI, Deep Learning, AR and VR will have major impacts on the Industrial Internet In-depth Interview On the promise of a future with the Industrial Internet The 4th Industrial Revolution is evolving at a terrific pace. Can you highlight some of the most notable aspects of Industry 4.0? The Industrial Internet is the 4th Industrial Revolution. It will have a profound impact on both the industrial productivity as well as the future of work. Due to more reliable power, cleaner water, and Intelligent Cities, the standard of living will improve, at large for the world citizens. Industrial Internet will forge new collaborations between the IT and OT, in the organizations, and each side will develop a better appreciation of the problems and technologies of the other. They will work together to create smoother overall operations by breaking the silos. On Shyam’s IIoT toolbox that he uses on a day to day basis You have a solid track record of architecting IIoT applications in the Big Data space over the years. What tools do you use on a day-to-day basis? In order to build Industrial Internet applications, GE's Predix is my preferred IIoT platform. It is built for Digital Industrial solutions, with security and compliance baked into it. Customer IIoT solutions can be quickly built on Predix and extended with the services in the marketplace from the ecosystem. For Asset Health Monitoring and for reducing the downtime, Asset Performance Management (APM) can be used to get a jump start and its extensibility framework can be used to extend it. On how to begin one’s journey into building the Industry 4.0 For an IIoT architect, what would your recommended learning plan be? What aspects of architecting Industry 4.0 applications are tricky to master and how does your book Architecting the Industrial Internet, prepare its readers to be industry ready? An IIoT Architect can start with the book Architecting the Industrial Internet, to get a good grasp of the area broadly. This book provides a diverse set of perspectives and architectural principles, from authors who work in GE Digital, Oracle and Microsoft. The end-to-end IIoT applications involve an understanding of sensors, machines, control systems, connectivity and cloud or server systems, along with the understanding of associated enterprise data, the architect needs to focus on a limited solution or proof of concept first. The book provides coverage for the end-to-end requirements of the IIoT solutions for the architects, developers and business managers. The extensive set of use cases and case studies provides examples from many different industry domains to allow the readers to easily related to it. The book is written, in a style that would not overwhelm the reader, yet explain the workings of the architecture and the solutions. The book will be best suited for Enterprise Architects and Data Architects who are trying to understand how IIoT solutions differ from traditional IT solutions. The layer-by-layer description of the IIoT Architecture will provide a systematic approach to help develop a deep understanding, for Architects. IoT Developers who have some understanding of this area can learn the IIoT platform-based approach to building solutions quickly. On how to choose the best technology solution to optimize ROI There are so many IIoT technologies, that manufacturers are confused as to how to choose the best technology to obtain the best ROI. What would your advice to manufacturers be, in this regard? The manufacturers and operation leaders look for quick solutions to known issues, in a proven way. Hence, often they do not have the appetite to experiment with a custom solution, rather they like to know where the solution provider has solved similar problems and what was the outcome. The collection of use cases and case studies will help business leaders get an idea of the potential ROI while evaluating the solution. Getting to know Predix, GE’s IIoT platform, better Let's talk a bit about Predix, GE's IIoT platform. What advantages does Predix offer developers and architects? Do you foresee any major improvements coming to Predix in the near future? The GE's Predix platform has a growing developer community that is approaching 40,000 strong. Likewise, the ecosystem of Partners is approaching 1000. Coupled with the free access to create developer accounts on Predix.io, the developers and architects can quickly learn from others and build working prototypes that can be used to get quick feedback from the business users. The catalog of microservices at Predix.io will continue to expand. Likewise, applications written on top of Predix, such as APM and OPM (Operations Performance Management) will continue to become feature-rich, providing coverage to many common Digital Industrial challenges. On the impact of other emerging technologies like AI on IIoT What according to you will the impact be of AI and Deep Learning, on IIoT? AI and Deep Learning help to build robust Digtal Twins of the industrial assets. These Digital Twins, will make the job of predictive maintenance and optimization, much easier for the operators of these assets. Further, IIoT will benefit from many new advances in technologies like AI, AR/VR and make the job of Field Services Technicians easier. IIoT is already widely used in energy generation and distribution, Intelligent Cities for law enforcement and to ease traffic congestion. The field of healthcare is evolving, due to increasing use of wearables. Finally, precision agriculture is enabled by IoT as well. On likely barriers to IIoT adoption What are the roadblocks you expect in the adoption of IIoT? Today the challenges to rapid adoption of IoT, are interoperability issues and lack of understanding of all the security ramifications of the hyper-connected world. Finally, how to explain the business case of the IoT to the decision makers and different stakeholders is still evolving. On why Architecting the Industrial Internet is a must read for Architects Would you like to give architects 3 reasons on why they should pick up your book? It is written by IIoT practitioners from large companies who are building solutions for both internal and external consumption. The book captures the architectural best practices and advocates a platform based approach, to solutions. The theory is put to practice in the form of use cases and case studies, to provide a comprehensive guide to the architects. If you enjoyed this interview, do check out Shyam’s latest book, Architecting the Industrial Internet.

At a time when software systems are growing in complexity, and when the expectations and demands from users have never been more critical, it's easy to forget that just making things work can be a huge challenge. That's where site reliability engineering (SRE) comes in; it's one of the reasons we're starting to see it grow as a discipline and job role. The central philosophy behind site reliability engineering can be seen in trends like chaos engineering. As Gremlin CTO Matt Fornaciari said, speaking to us in June, "chaos engineering is simply part of the SRE toolkit." For site reliability engineers, software resilience isn't an optional extra - it's critical. In crude terms, downtime for a retail site means real monetary losses, but the example extends beyond that. Because people and software systems are so interdependent, SRE is a useful way for thinking about how we build software more broadly. To get to the heart of what site reliability engineering is, I spoke to Nat Welch, an SRE currently working at First Look Media, whose experience includes time at Google and Hillary Clinton's 2016 presidential campaign. Nat has just published a book with Packt called Real-World SRE. You can find it here. Follow Nat on Twitter: @icco What is site reliability engineering? Nat Welch: The idea [of site reliability engineering] is to write and modify software to improve the reliability of a website or system. As a term and field, it was founded by Google in the early 2000s, and has slowly spread across the rest of the industry. Having engineers dedicated to global system health and reliability, working with every layer of the business to improving reliability for systems. "By building teams of engineers focused exclusively on reliability, there can be someone arguing for and focusing on reliability in a way to improve the speed and efficiency of product teams." Why do we need site reliability engineering? Nat Welch: Customers get mad if your website is down. Engineers often were having trouble weighing system reliability work versus new feature work. Because of this, product feature work often takes priority, and reliability decisions are made by guess work. By building teams of engineers focused exclusively on reliability, there can be someone arguing for and focusing on reliability in a way to improve the speed and efficiency of product teams. Why do we need SRE now, in 2018? Nat Welch: Part of it is that people are finally starting to build systems more like how Google has been building for years (heavy use of containers, lots of services, heavily distributed). The other part is a marketing effort by Google so that they can make it easier to hire. What are the core responsibilities of an SRE? How do they sit within a team? Nat Welch: SRE is just a specialization of a developer. They sit on equal footing with the rest of the developers on the team, because the system is everyone's responsbility. But while some engineers will focus primarily on new features, SRE will primarily focus on system reliability. This does not mean either side does not work on the other (SRE often write features, product devs often write code to make the system more reliable, etc), it just means their primary focus when defining priorities is different. What are the biggest challenges for site reliability engineers? Nat Welch: Communication with everyone (product, finance, executive team, etc.), and focus - it's very easy to get lost in fire fighting. What are the 3 key skills you need to be a good SRE? Nat Welch: Communication skills, software development skills, system design skills. You need to be able to write code, review code, work with others, break large projects into small pieces and distribute the work among people, but you also need to be able to take a system (working or broken) and figure out how it is designed and how it works. Thanks Nat! Site reliability engineering, then, is a response to a broader change in the types of software infrastructure we are building and using today. It's certainly a role that offers a lot of scope for ambitious and curious developers interested in a range of problems in software development, from UX to security. If you want to learn more, take a look at Nat's book.

Bo Weaver, a Kali Linux expert shares his thoughts on the security landscape in the cloud. He also talks about the skills gap in the current industry and why hiring is a tedious process. He explains the pitfalls in software development and where the tech is heading currently. Bo, along with another Kali Linux expert Wolf Halton were also interviewed on why Kali Linux is the premier platform for testing and maintaining Windows security. They talked about advantages and disadvantages for using Kali Linux for pentesting. We also asked them about what they think about pentesting in cybersecurity, in general. They have also talked about their stance about the role of pentesting in cybersecurity in their interview titled, “Security experts, Wolf Halton and Bo Weaver, discuss pentesting and cybersecurity” First is “The Cloud”.   I laugh and cry at this term.  I have a sticker on my laptop that says “There is no Cloud….  Only other people’s computers.”  Your data is sitting on someone else’s system along with other people’s data.  These other people also have access to this system. Sure security controls are in place but the security of “physical access” has been bypassed. [box type="shadow" align="" class="" width=""]You’re “in the box”.  One layer of security is now gone.  [/box] Also, your vendor has “FULL ACCESS” to your data in some cases.  How can you be sure what is going on with your data when it is in an unknown box in an unknown data center?  The first rule of security is “Trust No One”. Do you really trust Microsoft, Amazon, or Google? I sure don’t!!!  Having your data physically out of your company’s control is not a good idea. Yes, it is cheaper but what are your company and its digital property worth? The ‘real’ skills and hiring gap in tech For the knowledge and skills gap, I see the hiring process in this industry as the biggest gap.  The knowledge is out there. We now have schools that teach this field. When I started, there were no school courses.  You learned on your own. Since there is training, there are a lot of skilled people out there. But go looking for a job, and it is a nightmare.  IT doesn’t do the actual hiring these days. Either HR or a headhunting agency does the hiring. [box type="shadow" align="" class="" width=""]The people you talk to have no clue of what you really do.  They have a list of acronyms to look for that they have no clue about to match to your resume.  If you don’t have the complete list, you’re booted.[/box] If you don’t have a certain certification, you’re booted even if you’ve worked with the technology for 10 years.  Once, with my skill level, it took sending out over a thousand resumes and took over a year for me to find a job in the security field. The people you talk to have no clue of what you really do.  They have a list of acronyms to look for that they have no clue about to match to your resume.  If you don’t have the complete list, you’re booted. Also, when working in security, you can’t really talk about what you exactly did in your last job due to the NDA agreements with HR or a headhunter.  Writing these books is the first time I have been able to talk about what I do in detail since the network being breached is a lab network owned by myself.  XYZ bank would be really mad if I published their vulnerabilities and rightly so. In the US, most major networks are not managed by actual engineers but are managed by people with an MBA degree.  The manager has no clue of what they are actually managing. These managers are more worried about their department’s P&L statement than the security of the network.  In Europe, you find that the IT managers are older engineers that WORKED for years in IT and then moved to management. They fully understand the needs of a network and security. The trouble with software development In software development, I see a dumbing down of user interfaces.  This may be good for my 6-year-old grandson, but someone like me may want more access to the system.  I see developers change things just for the reason of “change”. Take Microsoft’s Ribbon in Office. Even after all these years, I find the ribbon confusing and hard to use.  At least, with Libre Office, they give you a choice between a ribbon and an old school menu bar. The changes in Gnome 3 from Gnome 2. This dumbing down and attempting to make a desktop usable for a tablet and a mouse totally destroyed the usability of their desktop.  What used to take 1 click now takes 4 clicks to do. [box type="shadow" align="" class="" width=""] A lot of developers these days have an “I am God and you are an idiot” complex.  Developers should remember that without an engineer, there would be no system for their code to run on.  Listen and work with engineers.[/box] Where do I see tech going?   Well, it is in everything these days and I don’t see this changing.  I never thought I would see a Linux box running a refrigerator or controlling my car, yet we do have them today.   Today, we can buy a system the size of a pack of cigarettes for less than $30.00 (Raspberry Pi) that can do more than a full-size server could do 10 years ago.  This is amazing. However, this is a two-edged sword when it comes to small, “cheap” devices. When you build a cheap device, you have to keep the costs down. For most companies building these devices, security is either non-existent or is an afterthought.  Yes, your whole network can be owned by first accessing that $30.00 IP camera with no security and moving on from there to eventually your Domain Controller. I know this works; I’ve done it several times. If you wish to further learn about tools which can improve your average in password acquisition, from hash cracking, online attacks, offline attacks, and rainbow tables to social engineering, the book Kali Linux 2018: Windows Penetration Testing - Second Edition is the go-to option for you. Author Bio Bo Weaver is an old school, ponytailed geek. His first involvement with networks was in 1972 while in the US Navy working on an R&D project called ARPA NET. Bo has been working with and using Linux daily since the 1990s and a promoter of Open Source. (Yes, Bo runs on Linux.) He now works as the senior penetration tester and security researcher for CompliancePoint an Atlanta based security consulting company. Cloud Security Tips: Locking Your Account Down with AWS Identity Access Manager (IAM) Cloud computing trends in 2019 Jeff Weiner talks about technology implications on society, unconscious bias, and skill gaps: Wired 25

In recent years, deep learning has made unprecedented progress in vision, speech, natural language processing and understanding, and other areas of data science. Developments in deep learning techniques, including GANs, variational autoencoders and deep reinforcement learning, are creating impressive AI results. For example, DeepMind's AlphaGo Zero became a game changer in AI research when it beat world champions in the game of Go. In this interview, Professor Rowel Atienza, author of the book Advanced Deep Learning with Keras talks about the recent developments in the field of deep learning. This book is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. This book strikes a balance between advanced concepts in deep learning and practical implementations with Keras. Key takeaways from the interview The intuition of deep learning is built on the fact that the deeper the network gets, the more feature representations the network learns in order to solve complex real-world problems. The objective of deep learning is to enable agents to be more robust to unforeseen events and to lessen the dependency on huge data. Advances in GANs enable us to generate high-dimensional fake data such as high-resolution images or videos that look very convincing. Deep learning tackles the curse of dimensionality by finding efficient data structures and layers that could represent complex data in the most efficient manner. The interview in detail What is the intuition behind deep learning? What are the recent developments in deep learning? Rowel Atienza: Deep learning is built on the intuition that the deeper the network gets, the more feature representations the network learns in order to solve complex real-world problems. Unlike machine learning, deep learning learns these features automatically from data in different degrees of supervision. There are many recent developments in deep learning. There are advances on graph neural networks because people are realizing the limits of NLP (Natural Language Processing), CNN (Convolution Neural Networks), and RNN (Recurrent Neural Networks) in representing more complex data structures such as social network, 3D shapes, molecular structures, etc. Implementing the causality in reasoning on data is another area of strong interest. Deep learning is strong on correlation not on discovering causality in data. Meta learning or learning to learn is also another area of interest. The objective is to enable agents to be more robust to unforeseen events and to lessen the dependency on huge data. What are different deep learning techniques to create successful AI? RA: A successful AI is dependent on two things: 1) deep domain knowledge and 2) deep understanding of state of the art techniques that will work on the domain problem. Domain knowledge comes from someone who is very familiar with the domain problem. This person is not necessarily knowledgeable in AI. This domain knowledge is then modelled in AI to automate the process of problem solving. How deep learning tackles the curse of dimensionality? RA: One of the goals of deep learning is to keep on finding efficient data structures and layers that could represent complex data in the most efficient manner. For example, geometric deep learning is able to circumvent the limitations of representing and learning from 3D data by avoiding inefficient 3D convolutions. There is still so much to be done in this space. What is autoencoders? What is the need of autoencoders in deep learning? How do you create an autoencoder? RA: Autoencoders compress high dimensionality data into low dimensionality code without losing important information. Low-dimensional code is suitable for further processing by other deep learning models such as in generative models like GANs and VAEs. Autoencoder can easily be implemented using two networks, an encoder and decoder. The depth, width, and type of layers are dependent on the original data to be encoded. Why are GANs so innovative? RA: GANs are innovative since they are good in generating fake data that look real. It is something that is hard to accomplish using other generative models. The advances in GANs enable us to generate high-dimensional fake data such as high resolution image or video that look very convincing. Tell us a little bit about this book? What makes this book necessary? What gap does it fill? RA: Advanced Deep Learning with Keras focuses on recent advances on deep learning It starts with a quick review of deep learning concepts (NLP, CNN, RNN). The discussions on deep neural networks, autoencoders, generative adversarial network (GAN), variational autoencoders (VAE), and deep reinforcement learning (DRL) follow. The book is important for everyone who would like to understand advanced concepts on deep learning and their corresponding implementation in Keras. The current version has in depth focus on generative models (autoencoders, GANs, VAEs) that could be used in-practical setting. The DRL explains the core concepts of value-based and policy-based methods in reinforcement learning and the corresponding working implementations in Keras which are difficult to make them right. About the Book Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. About the Author Rowel Atienza is an Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence. Rowel has been fascinated with intelligent robots since he graduated from the University of the Philippines. He received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution to the field of active gaze tracking for human-robot interaction. Deep learning models have massive carbon footprints, can photonic chips help reduce power consumption? Machine learning experts on how we can use machine learning to mitigate and adapt to the changing climate Google launches beta version of Deep Learning Containers for developing, testing and deploying ML applications

No one can seem to agree on what DevOps really is. Although it's been around for the better part of a decade, it still inspires a good deal of confusion within organizations and across engineering teams. But perhaps we're all over thinking it? To get to the heart of the issues and debates around DevOps, we spoke to Viktor Farcic in the latest episode of the Packt Podcast. Viktor is a consultant at CloudBees, but he's also a prolific author, having written multiple for books for Packt and other publishers. Most recently he helped put together the series of interviews that make up DevOps Paradox, which was published in June. Listen to the podcast here: https://soundcloud.com/packt-podcasts/why-devops-isnt-really-any-different-from-agile-an-interview-with-viktor-farcic Viktor Farcic on DevOps and agile and their importance in today's cloud-native world In the podcast, Farcic talks about a huge range of issues within DevOps. From the way the term itself has been used and misused by technology leaders, to its relationship to containers, cloud, and serverless, he provides some clarifications to what he sees as common misconceptions. What's covered in the podcast: What DevOps means today and its evolution over the last decade Its importance in the context of cloud and serverless DevOps tools Is DevOps a specialized role? Or is it something everyone that writes code should do? How it relates to roles like Site Reliability Engineering (SRE) Read next: DevOps engineering and full-stack development – 2 sides of the same agile coin What Viktor had to say... Viktor had this to say about the multiple ways in which DevOps is interpreted and practiced: "I work with a lot of companies, and every time I visit a company and they say “yes, we are doing DevOps” and I ask them “what is DevOps?” and I always get a different answer." This highlights that some clarification is long overdue when it comes to. Hopefully this conversation will go some way to doing just that...

Data analysis plays a vital role in organizations today. It enables effective decision-making by addressing fundamental business questions based on the understanding of the available data. While there are tons of open source and enterprise tools for conducting data analysis, IBM SPSS Statistics has emerged as a popular tool among statistical analysts and researchers. It offers them the perfect platform to quickly perform data exploration and analysis, and share their findings with ease. [author title=""]  Dr. Kenneth Stehlik-Barry Kenneth joined SPSS as Manager of Training in 1980 after using SPSS for his own research for several years. He has used SPSS extensively to analyze and discover valuable patterns that can be used to address pertinent business issues. He received his PhD in Political Science from Northwestern University and currently teaches in the Masters of Science in Predictive Analytics program there. Anthony J. Babinec Anthony joined SPSS as a Statistician in 1978 after assisting Norman Nie, the founder of SPSS, at the University of Chicago. Anthony has led a business development effort to find products implementing technologies such as CHAID decision trees and neural networks. Anthony received his BA and MA in Sociology with a specialization in Advanced Statistics from the University of Chicago and is on the Board of Directors of the Chicago Chapter of the American Statistical Association, where he has served in different positions including the President. [/author] In this interview, we take a look at the world of statistical data analysis and see how IBM SPSS Statistics makes it easier to derive business sense from data. Kenneth and Anthony also walk us through their recently published book - Data Analysis with IBM SPSS Statistics - and tell us how it benefits aspiring data analysts and statistical researchers. Key Takeaways - IBM SPSS Statistics IBM SPSS Statistics is a key offering of IBM Analytics - providing an integrated interface for statistical analysis on-premise and on the cloud SPSS Statistics is a self-sufficient tool - it does not require you to have any knowledge of SQL or any other scripting language SPSS Statistics helps you avoid the 3 most common pitfalls in data analysis, i.e. handling missing data, choosing the best statistical method for analysis and understanding the results of the analysis R and Python are not direct competitors to SPSS Statistics - instead, you can create customized solutions by integrating SPSS Statistics with these tools for effective analyses and visualization Data Analysis with IBM SPSS Statistics highlights various popular statistical techniques to the readers, and how to use them in order to gather useful hidden insights from their data Full Interview IBM SPSS Statistics is a popular tool for efficient statistical analysis. What do you think are the 3 notable features of SPSS Statistics that make it stand apart from the other tools available out there? SPSS Statistics has a very short learning curve which makes it ideal for analysts to use efficiently. It also has a very comprehensive set of statistical capabilities so virtually everything a researcher would ever need is encompassed in a single application. Finally, SPSS Statistics provides a wealth of features for preparing and managing data so it is not necessary to master SQL or another database language to address data-related tasks. With over 20 years of experience in this field, you have a solid understanding of the subject and, equally, of SPSS Statistics. How do you use the tool in your work? How does it simplify your day to day tasks related to data analysis? I have used SPSS Statistics in my work with SPSS and IBM clients over the years. In addition, I use SPSS for my own research analysis. It allows me to make good use of my time whether I'm serving clients or doing my own analysis because of the breadth of capabilities available within this one program. The fact that SPSS produces presentation-ready output further simplifies things for me since I can collect key results as I work and put them into a draft report and share them as required. What are the prerequisites to use SPSS Statistics effectively? For someone who intends to use SPSS Statistics for their data analysis tasks, how steep is the curve when it comes to mastering the tool? It certainly helps to have a understanding of basic statistics when you begin to use SPSS Statistics but it can be a valuable tool even with a limited background in statistics. The learning curve is a very "gentle slope" when it comes to acquiring sufficient familiarity with SPSS Statistics to use it very effectively. Mastering the software does involve more time and effort but one can accomplish this over time as one builds on the initial knowledge that comes fairly easily. The good news is that one can obtain a lot of value from the software well before one truly masters it by discovering the many features.   What are some of the common problems in data analysis? How does this book help the readers overcome them? Some of the most common pitfalls encountered when analyzing data involve handling missing/incomplete data, deciding which statistical method(s) to employ and understanding the results. In the book, we go into the details of detecting and addressing data issues including missing data. We also describe what each statistical technique provides and when it is most appropriate to use each of them. There are numerous examples of SPSS Statistics output and how the results can be used to assess whether a meaningful pattern exists. In the context of all the above, how does your book Data Analysis with IBM SPSS Statistics help readers in their statistical analysis journey? What, according to you, are the 3 key takeaways for the readers from this book? The approach we took with our book was to share with readers the most straightforward ways to use SPSS Statistics to quickly obtain the results needed to effectively conduct data analysis. We did this by showing the best way to proceed when it comes to analyzing data and then showing how this process can be done best in the software. The key takeaways from our book are the way to approach the discovery process when analyzing data, how to find hidden patterns present in the data and what to look for in the results provided by the statistical techniques covered in the book.   IBM SPSS Statistics 25 was released recently. What are the major improvements or features introduced in this version? How do these features help the analysts and researchers? There are a lot of interesting new features introduced in SPSS Statistics 25. For starters, you can copy charts as Microsoft Graphic Objects, which allows you to manipulate charts in Microsoft Office. There are changes to the chart editor that make it easier to customize colors, borders, and grid line settings in charts. Most importantly, it allows the implementation of Bayesian statistical methods. Bayesian statistical methods enable the researcher to incorporate prior knowledge and assumptions about model parameters. This facility looks like a good teaching tool for Statistical Educators. Data visualization goes a long way in helping decision-makers get an accurate sense of their data. How does SPSS Statistics help them in this regard? Kenneth: Data visualization is very helpful when it comes to communicating findings to a broader audience and we spend time in the book describing when and how to create useful graphics to use for this purpose. Graphical examination of the data can also provide clues regarding data issues and hidden patterns that warrant deeper exploration. These topics are also covered in the book. Anthony: SPSS Statistics’ data visualizations capabilities are excellent. The menu system makes it easy to generate common chart types. You can develop customized looks and save them as a template to be applied to future charts. Underlying SPSS Graphics is an influential approach called the Grammar of Graphics. The SPSS graphics capabilities are embodied in a versatile syntax called Graphics Programming Language. Do you foresee SPSS Statistics facing stiff competition from open source alternatives in the near future? What is the current sentiment in the SPSS community regarding these topics? Kenneth: Open source tools based alternatives such as Python and R are potential competition for SPSS Statistics but I would argue otherwise. These tools, while powerful, have a much steeper learning curve and will prove difficult for subject matter experts that periodically need to analyze data. SPSS is ideally suited for these periodic analysts whose main expertise lies in their field which could be healthcare, law enforcement, education, human resources, marketing, etc. Anthony: The open source programs have a lot of capability but they are also fairly low-level languages, so you must learn to code. The learning curve is steep, and there are many maintainability issues. R has 2 major releases a year. You can have a situation where the data and commands remain the same, but the result changes when you update R. There are many dependencies among R packages. R has many contributors and is an avenue for getting your hands on new methods. However, there is a wide variance in the quality of the contributors and contributed packages. The occasional user of SPSS has an easier time jumping back in than does the occasional user of open source software. Most importantly, it is easier to employ SPSS in production settings. SPSS Statistics supports custom analytical solutions through integration with R and Python. Is this an intent from IBM to join hands with the open source community? This is a good follow-up question to the one asked before. Actually, the integration with R and Python allows SPSS Statistics to be extended to accommodate a situation in which an analyst wishes to try an algorithm or graphical technique not directly available in the software but which is supported in one of these languages. It also allows those familiar with R or Python to use SPSS Statistics as their platform and take advantage of all the built-in features it comes with, out of the box while still having the option to employ these other languages where they provide additional value. Lastly, this book is designed for analysts and researchers who want to get meaningful insights from their data as quickly as possible. How does this book help them in this regard? SPSS Statistics does make it possible to very quickly pull in data and get insightful results. This book is designed to streamline the steps involved in getting this done while also pointing out some of the less obvious "hidden gems" that we have discovered during the decades of using SPSS in virtually every possible situation.

"Open source back in the late nineties - and even throughout the 2000s - was really hard to use," ActiveState CEO Bart Copeland says. "Our job," he continues, "was to make it much easier for developers to use open source and much easier for enterprises to use open source." How does ActiveState work? But how does ActiveState actually do this? Copeland explains: "ActiveState is exactly like Red Hat. So what Red Hat did to Linux - providing enterprise-grade Linux distributions - ActiveState does for open source programming languages." Clearly ActiveState is an interesting product that's playing an important part in helping enterprises to better manage the widespread migration to open source technology. For the latest edition of the Packt Podcast we spoke to Copeland about ActiveState and the growth of open source over the last decade. We think you'll find what he has to say interesting... Listen: https://soundcloud.com/packt-podcasts/activestate-making-open-source-more-accessible-for-the-enterprise-interview-with-bart-copeland   Read next: Can a modified MIT ‘Hippocratic License’ to restrict misuse of open source software prompt a wave of ethical innovation in tech? Key quotes from Bart Copeland Copeland on the relationship between enterprise management and developers: "If you look at the enterprise… they want to make sure that it works and it doesn’t cause security threats and their in compliance with all the licenses. And the result is, due to the complexities of open source, management within the enterprise will often limit developers on what languages and what open source stacks they can use because the more stacks you have, the more complexity you have in an organization." Copeland on developer freedom: "A developer is a very technical and creative individual and they want to be able to use the right tools to build the right solution. And so if a developer is handcuffed to certain technology stacks, they may not be able to use the best technology to solve the problem." Learn more about ActiveState here.

Data is the new oil and is just as crude as unrefined oil. To do anything meaningful - modeling, visualization, machine learning, for predictive analysis – you first need to wrestle and wrangle with data. We recently interviewed Dr. Tirthajyoti Sarkar and Shubhadeep Roychowdhury, the authors of the course Data Wrangling with Python. They talked about their new course and discuss why do data wrangling and why use Python to do it. Key Takeaways Python boasts of a large, broad library equipped with a rich set of modules and functions, which you can use to your advantage and manipulate complex data structures NumPy, the Python library for fast numeric array computations and Pandas, a package with fast, flexible, and expressive data structures are helpful in working with “relational” or “labeled” data. Web scraping or data extraction becomes easy and intuitive with Python libraries, such as BeautifulSoup4 and html5lib. Regex, the tiny, highly specialized programming language inside Python can create patterns that help match, locate, and manage text for large data analysis and searching operations Present interesting, interactive visuals of your data with Matplotlib, the most popular graphing and data visualization module for Python. Easily and quickly separate information from a huge amount of random data using Pandas, the preferred Python tool for data wrangling and modeling. Full Interview Congratulations on your new course ‘Data wrangling with Python’. What this course is all about? Data science is the ‘sexiest job’ of 21st century’ (at least until Skynet takes over the world). But for all the emphasis on ‘Data’, it is the ‘Science’ that makes you - the practitioner - valuable. To practice high-quality science with data, first you need to make sure it is properly sourced, cleaned, formatted, and pre-processed. This course teaches you the most essential basics of this invaluable component of the data science pipeline – data wrangling. What is data wrangling and why should you learn it well? “Data is the new Oil” and it is ruling the modern way of life through incredibly smart tools and transformative technologies. But oil from the rig is far from being usable. It has to be refined through a complex processing network. Similarly, data needs to be curated, massaged and refined to become fit for use in intelligent algorithms and consumer products. This is called “wrangling” and (according to CrowdFlower) all good data scientists spend almost 60-80% of their time on this, each day, every project. It generally involves the following: Scraping the raw data from multiple sources (including web and database tables), Inputing, formatting, transforming – basically making it ready for use in the modeling process (e.g. advanced machine learning), Handling missing data gracefully, Detecting outliers, and Being able to perform quick visualizations (plotting) and basic statistical analysis to judge the quality of your formatted data This course aims to teach you all the core ideas behind this process and to equip you with the knowledge of the most popular tools and techniques in the domain. As the programming framework, we have chosen Python, the most widely used language for data science. We work through real-life examples and at the end of this course, you will be confident to handle a myriad array of sources to extract, clean, transform, and format your data for further analysis or exciting machine learning model building. Walk us through your thinking behind how you went about designing this course. What’s the flow like? How do you teach data wrangling in this course? The lessons start with a refresher on Python focusing mainly on advanced data structures, and then quickly jumping into NumPy and Panda libraries as fundamental tools for data wrangling. It emphasizes why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of specialized pre-built routines in Python. Thereafter, it covers how using the same Python backend, one can extract and transform data from a diverse array of sources - internet, large database vaults, or Excel financial tables. Further lessons teach how to handle missing or wrong data, and reformat based on the requirement from a downstream analytics tool. The course emphasizes learning by real example and showcases the power of an inquisitive and imaginative mind primed for success. What other tools are out there? Why do data wrangling with Python? First, let us be clear that there is no substitute for the data wrangling process itself. There is no short-cut either. Data wrangling must be performed before the modeling task but there is always the debate of doing this process using an enterprise tool or by directly using a programming language and associated frameworks. There are many commercial, enterprise-level tools for data formatting and pre-processing, which does not involve coding on the part of the user. Common examples of such tools are: General purpose data analysis platforms such as Microsoft Excel (with add-ins) Statistical discovery package such as JMP (from SAS) Modeling platforms such as RapidMiner Analytics platforms from niche players focusing on data wrangling such as – Trifacta, Paxata, Alteryx At the end of the day, it really depends on the organizational approach whether to use any of these off-the-shelf tools or to have more flexibility, control, and power by using a programming language like Python to perform data wrangling. As the volume, velocity, and variety (three V’s of Big Data) of data undergo rapid changes, it is always a good idea to develop and nurture significant amount of in-house expertise in data wrangling. This is done using fundamental programming frameworks so that an organization is not betrothed to the whims and fancies of any particular enterprise platform as a basic task as data wrangling. Some of the obvious advantages of using an open-source, free programming paradigm like Python for data wrangling are: General purpose open-source paradigm putting no restriction on any of the methods you can develop for the specific problem at hand Great eco-system of fast, optimized, open-source libraries, focused on data analytics Growing support to connect Python for every conceivable data source types, Easy interface to basic statistical testing and quick visualization libraries to check data quality Seamless interface of the data wrangling output to advanced machine learning models – Python is the most popular language of choice of machine learning/artificial intelligence these days. What are some best practices to perform data wrangling with Python? Here are five best practices that will help you out in your data wrangling journey with Python. And in the end, all you’ll have is clean and ready to use data for your business needs. Learn the data structures in Python really well Learn and practice file and OS handling in Python Have a solid understanding of core data types and capabilities of Numpy and Pandas Build a good understanding of basic statistical tests and a panache for visualization Apart from Python, if you want to master one language, go for SQL What are some misconceptions about data wrangling? Though data wrangling is an important task, there are certain myths associated with data wrangling which developers should be cautious of. Myths such as: Data wrangling is all about writing SQL query Knowledge of statistics is not required for data wrangling You have to be a machine learning expert to do great data wrangling Deep knowledge of programming is not required for data wrangling Learn in detail about these misconceptions. We hope that these misconceptions would help you realize that data wrangling is not as difficult as it seems. Have fun wrangling data! About the authors Dr. Tirthajyoti Sarkar works as a Sr. Principal Engineer in the semiconductor technology domain where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. Shubhadeep Roychowdhury works as a Sr. Software Engineer at a Paris based Cyber Security startup. He holds a Master Degree in Computer Science from West Bengal University Of Technology and certifications in Machine Learning from Stanford. 5 best practices to perform data wrangling with Python 4 misconceptions about data wrangling Data cleaning is the worst part of data analysis, say data scientists

Management is a discipline that isn't short of coverage. In fact, it's probably fair to say that the world throws too much attention its way. This only serves to muddy the waters of management principles and make it hard to determine what really matters. To complicate things, technology is ripping up the rule book when it comes to processes and hierarchies. Programming and engineering are forcing management gurus to rethink what it means to 'manage' today. However, while the wealth of perspectives on modern management amount to a bit of a cacophony, looking specifically at what it means to be a manager in a world defined by software can be useful. That's why, in the latest episode of the Packt Podcast, we spoke to Herman Fung. Herman is someone with both development and management experience, and, following the publication of his book The Successful Software Manager earlier this year, he's been spending a lot of time seriously considering what it means to be a software manager. Listen to the podcast episode: https://soundcloud.com/packt-podcasts/what-does-it-mean-to-be-a-software-manager-herman-fung-explains Some of the topics covered in this episode include: How to approach software management if you haven't done it before The impact of Agile and DevOps What makes managing in the context of engineering and technology different from other domains The differences between leadership and management You can buy The Successful Software Manager from the Packt store as a print or eBook. Click here. Follow Herman on Twitter: @FUNG14