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Data Science Software
Data science software is a collection of tools and platforms designed to facilitate the analysis, interpretation, and visualization of large datasets, helping data scientists derive insights and build predictive models. These tools support various data science processes, including data cleaning, statistical analysis, machine learning, deep learning, and data visualization. Common features of data science software include data manipulation, algorithm libraries, model training environments, and integration with big data solutions. Data science software is widely used across industries like finance, healthcare, marketing, and technology to improve decision-making, optimize processes, and predict trends.
Computer Vision Software
Computer vision software allows machines to interpret and analyze visual data from images or videos, enabling applications like object detection, image recognition, and video analysis. It utilizes advanced algorithms and deep learning techniques to understand and classify visual information, often mimicking human vision processes. These tools are essential in fields like autonomous vehicles, facial recognition, medical imaging, and augmented reality, where accurate interpretation of visual input is crucial. Computer vision software often includes features for image preprocessing, feature extraction, and model training to improve the accuracy of visual analysis. Overall, it enables machines to "see" and make informed decisions based on visual data, revolutionizing industries with automation and intelligence.
AI Coding Assistants
AI coding assistants are software tools that use artificial intelligence to help developers write, debug, and optimize code more efficiently. These assistants typically offer features like code auto-completion, error detection, suggestion of best practices, and code refactoring. AI coding assistants often integrate with integrated development environments (IDEs) and code editors to provide real-time feedback and recommendations based on the context of the code being written. By leveraging machine learning and natural language processing, these tools can help developers increase productivity, reduce errors, and learn new programming techniques.
View more categories (6) for "python regex"
21 Products for "python regex" with 3 filters applied:
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1
MLJAR Studio
MLJAR
...Install needed modules with 1-click, literally. You can create and interact with all variables available in your Python session. Interactive recipes speed-up your work. AI Assistant has access to your current Python session, variables and modules. Broad context makes it smart. Our AI Assistant was designed to solve data problems with Python programming language. It can help you with plots, data loading, data wrangling, Machine Learning and more. Use AI to quickly solve issues with code, just click Fix button. ...Starting Price: $20 per month -
2
Teradata VantageCloud
Teradata
...Designed for scalability and flexibility, VantageCloud supports multi-cloud and hybrid deployments, enabling organizations to manage structured and semi-structured data across AWS, Azure, Google Cloud, and on-premises systems. It offers full ANSI SQL support, integrates with open-source tools like Python and R, and provides built-in governance for secure, trusted AI. VantageCloud empowers users to run complex queries, build data pipelines, and operationalize machine learning models—all while maintaining interoperability with modern data ecosystems. -
3
Plotly Dash
Plotly
Dash & Dash Enterprise let you build & deploy analytic web apps using Python, R, and Julia. No JavaScript or DevOps required. Through Dash, the world's largest companies elevate AI, ML, and Python analytics to business users at 5% the cost of a full-stack development approach. Deliver apps and dashboards that run advanced analytics: ML, NLP, forecasting, computer vision and more. Work in the languages you love: Python, R, and Julia. -
4
Daft
Daft
Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend. -
5
Streamlit
Streamlit
Streamlit. The fastest way to build and share data apps. Turn data scripts into sharable web apps in minutes. All in Python. All for free. No front-end experience required. Streamlit combines three simple ideas. Embrace Python scripting. Build an app in a few lines of code with our magically simple API. Then see it automatically update as you save the source file. Weave in interaction. Adding a widget is the same as declaring a variable. No need to write a backend, define routes, handle HTTP requests, etc. ... -
6
NVIDIA RAPIDS
NVIDIA
...Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently. -
7
Quadratic
Quadratic
Quadratic enables your team to work together on data analysis to deliver faster results. You already know how to use a spreadsheet, but you’ve never had this much power. Quadratic speaks Formulas and Python (SQL & JavaScript coming soon). Use the language you and your team already know. Single-line formulas are hard to read. In Quadratic you can expand your recipes to as many lines as you need. Quadratic has Python library support built-in. Bring the latest open-source tools directly to your spreadsheet. The last line of code is returned to the spreadsheet. ... -
8
Cloudera Data Science Workbench
Cloudera
Accelerate machine learning from research to production with a consistent experience built for your traditional platform. With Python, R, and Scala directly in the web browser, Cloudera Data Science Workbench (CDSW) delivers a self-service experience data scientists will love. Download and experiment with the latest libraries and frameworks in customizable project environments that work just like your laptop. Cloudera Data Science Workbench provides connectivity not only to CDH and HDP but also to the systems your data science teams rely on for analysis. ... -
9
Dask
Dask
Dask is open source and freely available. It is developed in coordination with other community projects like NumPy, pandas, and scikit-learn. Dask uses existing Python APIs and data structures to make it easy to switch between NumPy, pandas, scikit-learn to their Dask-powered equivalents. Dask's schedulers scale to thousand-node clusters and its algorithms have been tested on some of the largest supercomputers in the world. But you don't need a massive cluster to get started. Dask ships with schedulers designed for use on personal machines. ... -
10
Metaflow
Metaflow
Successful data science projects are delivered by data scientists who can build, improve, and operate end-to-end workflows independently, focusing more on data science, less on engineering. Use Metaflow with your favorite data science libraries, such as Tensorflow or SciKit Learn, and write your models in idiomatic Python code with not much new to learn. Metaflow also supports the R language. Metaflow helps you design your workflow, run it at scale, and deploy it to production. It versions and tracks all your experiments and data automatically. It allows you to inspect results easily in notebooks. Metaflow comes packaged with the tutorials, so getting started is easy. ... -
11
Microsoft R Open
Microsoft
Microsoft continues its commitment and development in R, not only in the latest Machine Learning Server release, but also in the newest Microsoft R Client and Microsoft R Open releases. You can also find R and Python support in SQL Server Machine Learning Services on Windows and Linux, and R support in Azure SQL Database. R components are backwards compatible. You should be able to run existing R script on newer versions, with the exception of dependencies on packages or platforms that are no longer supported, or known issues that require a workaround or code change. ... -
12
Google Colab
Google
...It offers no-setup, easy access to computational resources such as GPUs and TPUs, making it ideal for users working with data-intensive projects. Colab allows users to run Python code in an interactive, notebook-style environment, share and collaborate on projects, and access extensive pre-built resources for efficient experimentation and learning. Colab also now offers a Data Science Agent automating analysis, from understanding the data to delivering insights in a working Colab notebook (Sequences shortened. ... -
13
esDynamic
eShard
Maximize your security testing journey, from setting up your bench to analyzing your data processing results, esDynamic saves you valuable time and effort, empowering you to unleash the full potential of your attack workflow. Discover the flexible and comprehensive Python-based platform, perfectly suited for every phase of your security analysis. Customize your research space to meet your unique requirements by effortlessly adding new equipment, integrating tools, and modifying data. Additionally, esDynamic features an extensive collection of materials on complex topics that would typically require extensive research or a team of specialists, granting you instant access to expertise. ...Starting Price: Free -
14
Azure Data Science Virtual Machines
Microsoft
...Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.Starting Price: $0.005 -
15
Oracle Machine Learning
Oracle
...Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression. ... -
16
SAS Viya
SAS
...This facilitates collaboration across your organization because users can program in their language of choice. You can also take advantage of SAS Deep Learning with Python (DLPy), our open-source package on GitHub. -
17
Zerve AI
Zerve AI
...Zerve’s data science development environment gives data science and ML teams a unified space to explore, collaborate, build, and deploy data science & AI projects like never before. Zerve offers true language interoperability, meaning that as well as being able to use Python, R, SQL, or Markdown all in the same canvas, users can connect these code blocks to each other. No more long-running code blocks or containers, with Zerve enjoying unlimited parallelization at any stage of the development journey. Analysis artifacts are automatically serialized, versioned, stored, and preserved for later use, meaning easily changing a step in the data flow without needing to rerun any preceding steps. ... -
18
Zepl
Zepl
...Zepl’s powerful search lets you discover and reuse models and code. Use Zepl’s enterprise collaboration platform to query data from Snowflake, Athena or Redshift and build your models in Python. Use pivoting and dynamic forms for enhanced interactions with your data using heatmap, radar, and Sankey charts. Zepl creates a new container every time you run your notebook, providing you with the same image each time you run your models. Invite team members to join a shared space and work together in real time or simply leave their comments on a notebook. ... -
19
Deepnote
Deepnote
...Features: - Sharing notebooks and projects via URL - Inviting others to view, comment and collaborate, with version control - Publishing notebooks with visualizations for presentations - Sharing datasets between projects - Set team permissions to decide who can edit vs view code - Full linux terminal access - Code completion - Automatic python package management - Importing from github - PostgreSQL DB connectionStarting Price: Free -
20
Hex
Hex
Hex brings together the best of notebooks, BI, and docs into a seamless, collaborative UI. Hex is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories. Your default landing page in Hex is the Projects page. You can quickly find projects you created, as well as those shared with you and your workspace. The outline provides an easy-to-browse overview of all the cells in a project's Logic View. Every cell in the outline lists the variables it defines, and cells that return a displayed output (chart cells, Input Parameters, markdown cells, etc.) display a preview of that output. ...Starting Price: $24 per user per month -
21
...Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.
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