MLOps

Building Agentic AI systems goes far beyond connecting a few LLMs. It requires a multi-layered blueprint—one that ensures performance, trust, scalability, and alignment with human needs. Here’s a breakdown of the eight core layers in the reference architecture: 𝟭. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗟𝗮𝘆𝗲𝗿 (𝗕𝗮𝘀𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻) The raw power behind AI systems. • On-prem & cloud GPUs (NVIDIA A100/H100, AWS, Azure, GCP) • Specialized accelerators (TPUs, Graphcore, Habana Gaudi) • Containerization & virtualization (Docker...

35 practical thoughts on system design Core Principles 1. Every system is a trade-off -> you never get speed, cost, and simplicity all at once. 2. Latency compounds -> every millisecond added across layers adds up to user pain. 3. Scalability ≠ performance -> one handles growth, the other handles speed. 4. Read vs. write paths -> scaling each requires completely different strategies. 5. Design for change, not perfection -> requirements will shift. Databases & Storage 6. Indexes are levers...

Excited to share this comprehensive MLOps architecture diagram! It beautifully breaks down the journey of an ML project from initial concept to production and continuous improvement. Key takeaways for me include: A. MLOps Project Initiation: Starting with clear business problem analysis and identifying the right technologies. B1. Requirements for ML Engineering Pipeline: Defining transformation and cleaning rules – essential for robust data. B2. Feature Engineering Pipeline: Connecting to...

𝐃𝐞𝐯𝐒𝐞𝐜𝐎𝐩𝐬 𝐂𝐲𝐜𝐥𝐞 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 In today's rapidly evolving digital landscape, integrating security at every phase of the development lifecycle is no longer optional; it's essential. Enter DevSecOps, a methodology that ensures security is a foundational component throughout the entire DevOps process. 𝐎𝐩𝐞𝐫𝐚𝐭𝐞 & 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 RASP (Runtime Application Self-Protection): Continuously monitors and protects applications in real-time, detecting and mitigating threats. Audit: Regularly review and analyze logs...

I have been developing Agentic Systems for more than two years now and the same patterns keep emerging. 👇 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 is the only way how you can be successful in building your 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 - here is my template. Let’s zoom in: 𝟭. Define a problem you want to solve: is GenAI even needed? 𝟮. Build a Prototype: figure out if the solution is feasible. 𝟯. Define Performance Metrics: you must have output metrics defined for how you will measure success of your application. 𝟰...

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As an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 you should also care about regular, non LLM-based ML models, productionising them comes with its own challenges. For example, 𝗖𝗜/𝗖𝗗 process is 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 compared to regular software. The important difference that the Machine Learning aspect of the projects brings to the CI/CD process is the treatment of the Machine Learning Training pipeline as a first class citizen of the software world. ➡️ CI/CD pipeline is a separate entity from Machine...

This repo covers everything you need to know about MLOps. The goal of the series is to understand the basics of MLOps like model building, monitoring, configurations, testing, packaging, deployment, cicd, etc. Week 0: Project Setup Week 1: Model Monitoring - Weights and Biases Week 2: Configurations - Hydra Week 3: Data Version Control - DVC Week 4: Model Packaging - ONNX Week 5: Model Packaging - Docker Week 6: CI/CD - GitHub Actions Week 7: Container Registry - AWS ECR Week 8: Serverless...

This repo covers everything you need to know about MLOps! It covers every stage of the pipeline, including model building, monitoring, configurations… | 22 comments on LinkedIn

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500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

As an 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 you should also care about regular, non LLM-based ML models, productionising them comes with its own challenges. For example, 𝗖𝗜/𝗖𝗗 process is 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 compared to regular software. The important difference that the Machine Learning aspect of the projects brings to the CI/CD process is the treatment of the Machine Learning Training pipeline as a first class citizen of the software world. ➡️ CI/CD pipeline is a separate entity from Machine...

I'm thrilled to share my latest project: a real-time Customer Purchase Prediction ML system, built from scratch using cutting-edge MLOps practices and modern… | 15 comments on LinkedIn

Here’s the hardest challenge when deploying ML models: (And it’s not achieving high accuracy) 𝗜𝘁’𝘀 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗶𝗻…

The most complex step in AI/ML in production? (You can probably guess) Serving the ML models. Why is it so tough? Because you have to put all the pieces… | 11 comments on LinkedIn