MLOps Part 1: An introduction to machine learning in production, covering pitfalls, system-level concerns, and an overview of the full ML lifecycle.
MCP Part 9: Building a full-fledged research assistant with MCP and LangGraph.
MCP Part 8: Integration of the model context protocol (MCP) with LangGraph, LlamaIndex, CrewAI, and PydanticAI.
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.
MCP Part 6: An overview of testing using the MCP Inspector, and a discussion of common vulnerabilities, mitigation strategies, and MCP Roots.
MCP Part 5: A deep dive into sampling, its working, code, use cases and best practices.
MCP Part 4: An in-depth exploration of MCP resources and prompts, followed by a hands-on demonstration of an MCP server utilizing tools, resources, and prompts for job search and analysis.
