367 posts published
MLOps Part 7: An applied look at distributed data processing with Spark and workflow orchestration and scheduling with Prefect.
100% local.
100% local.
MLOps Part 6: A deep dive into sampling, class imbalance, and data leakage; plus a hands-on Feast feature store demo.
MLOps Part 5: A detailed walkthrough of data engineering for MLOps, covering data sources, format performance trade-offs, and ETL/ELT pipelines.
MLOps Part 4: A practical walkthrough of W&B-powered reproducibility.
...explained visually and with code.
MLOps Part 3: A practical exploration of reproducibility and versioning, covering deterministic training, data and model versioning, and experiment tracking.
...explained step-by-step with code.
MLOps Part 2: A deeper look at the ML lifecycle, plus a minimal train-to-API and containerization demo using FastAPI and Docker.
...explained step-by-step with code.
MLOps Part 1: An introduction to machine learning in production, covering pitfalls, system-level concerns, and an overview of the full ML lifecycle.
...explained visually.
MCP Part 9: Building a full-fledged research assistant with MCP and LangGraph.
...explained step-by-step with code.
..powered with MCP + Tools + Memory + Observability.
MCP Part 8: Integration of the model context protocol (MCP) with LangGraph, LlamaIndex, CrewAI, and PydanticAI.
Hands-on demo.
MCP Part 7: A deep dive into understanding sandboxing and its need in MCP.
Understanding every little detail on vector databases and their utility in LLMs, along with a hands-on demo.
![[Hands-on] Make RAG systems 32x memory efficient!](/content/images/size/w800/2025/06/zep-claude-cursor.gif)
