MLA 024 Code AI MCP Servers, ML Engineering Machine Learning Guide podcast

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MLA 024 Code AI MCP Servers, ML Engineering

14d ago 44:17

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Notes and resources at ocdevel.com/mlg/mla-24
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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.

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