MLA 024 Code AI MCP Servers, ML Engineering

Model Context Protocol (MCP) standardizes tool communication, enabling AI coding agents to perform complex tasks like executing commands, interacting with web browsers, and integrating local or cloud resources. MCP servers broaden AI applications beyond coding. In machine learning, use AI tools to help optimizing data engineering, model deployment, and augmenting typical machine learning tasks. Links • Notes and resources at ocdevel.com/mlg/mla-24 Try a walking desk • stay healthy & sharp while you learn & code Try Descript • audio/video editing with AI power-tools Tool Use in AI Code Agents File Operations • : Agents can read, edit, and search files using sophisticated regular expressions. Executable Commands • : They can recommend and perform installations like pip • or npm • installs, with user approval. Browser Integration • : Allows agents to perform actions and verify outcomes through browser interactions. Model Context Protocol (MCP) Standardization • : MCP was created by Anthropic to standardize how AI tools and agents communicate with each other and with external tools. Implementation • : MCP Client • : Converts AI agent requests into structured commands. MCP Server • : Executes commands and sends structured responses back to the client. • Local and Cloud Frameworks • : Local (S-T-D-I-O MCP) • : Examples include utilizing Playwright for local browser automation and connecting to local databases like Postgres. Cloud (SSE MCP) • : SaaS providers offer cloud-hosted MCPs to enhance external integrations. • Expanding AI Capabilities with MCP Servers Directories • : Various directories exist listing MCP servers for diverse functions beyond programming. modelcontextprotocol/servers Use Cases • : Automation Beyond Coding • : Implementing MCPs that extend automation into non-programming tasks like sales, marketing, or personal project management. Creative Solutions • : Encourages innovation in automating routine tasks by integrating diverse MCP functionalities. • AI Tools in Machine Learning Automating ML Process • : Auto ML and Feature Engineering • : AI tools assist in transforming raw data, optimizing hyperparameters, and inventing new ML solutions. Pipeline Construction and Deployment • : Facilitates the use of infrastructure as code for deploying ML models efficiently. • Active Experimentation • : Jupyter Integration Challenges • : While integrations are possible, they often lag and may not support the latest models. Practical Strategies • : Suggests alternating between Jupyter and traditional Python files to maximize tool efficiency. • Action Plan for ML Engineers • : • Setup structured folders and documentation to leverage AI tools effectively. • Encourage systematic exploration of MCPs to enhance both direct programming tasks and associated workflows. •