Optimizing Machine Learning Development with Code AI Tools

Tool use in code AI agents allows for both in-editor code completion and agent-driven file and command actions, while the Model Context Protocol (MCP) standardizes how these agents communicate with external and internal tools. MCP integration broadens the automation capabilities for developers and machine learning engineers by enabling access to a wide variety of local and cloud-based tools directly within their coding environments.

Links

• Notes and resources at ocdevel.com/mlg/mla-24
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Tool Use in Code AI Agents

• Code AI agents offer two primary modes of interaction: in-line code completion within the editor and agent interaction through sidebar prompts.
• Inline code completion has evolved from single-line suggestions to cross-file edits, refactoring, and modification of existing code blocks.
• Tools accessible via agents include read, write, and list file functions, as well as browser automation and command execution; permissions for sensitive actions can be set by developers.
• Agents can intelligently search a project’s codebase and dependencies using search commands and regular expressions to locate relevant files.

Model Context Protocol (MCP)

• MCP, introduced by Anthropic, establishes a standardized protocol for agents to communicate with tools and services, replacing bespoke tool integrations.
• The protocol is analogous to REST for web servers and unifies tool calling for both local and cloud-hosted automation.
• MCP architecture involves three components: the AI agent, MCP client, and MCP server. The agent provides context, the client translates requests and responses, and the server executes and responds with data in a structured format.
• MCP servers can be local (STDIO-based for local tasks like file search or browser actions) or cloud-based (SSE for hosted APIs and SaaS tools).
• Developers can connect code AI agents to directories of MCP servers, accessing an expanding ecosystem of automation tools for both programming and non-programming tasks.

MCP Application Examples

• Local MCP servers include Playwright for browser automation and Postgres MCP for live database schema analysis and data-driven UI suggestions.
• Cloud-based MCP servers integrate APIs such as AWS, enabling infrastructure management directly from coding environments.
• MCP servers are not limited to code automation; they are widely used for pipeline automation in sales, marketing, and other internet-connected workflows.

Retrieval Augmented Generation (RAG) as an MCP Use Case

• RAG, once standard in code AI tools, indexed codebases using embeddings to assist with relevant file retrieval, but many agents now favor literal search for practicality.
• Local RAG MCP servers, such as Chroma or LlamaIndex, can index entire documentation sets to update agent knowledge of recent or project-specific libraries outside of widely-known frameworks.
• Fine-tuning a local LLM with the same documentation is an alternative approach to integrating new knowledge into code AI workflows.

Machine Learning Applications

• Code AI tooling supports feature engineering, data cleansing, pipeline setup, model design, and hyperparameter optimization, based on real dataset distributions and project specifications.
• Agents can recommend advanced data transformations—such as Yeo-Johnson power transformation for skewed features—by directly analyzing example dataset distributions.
• Infrastructure-as-code integration enables rapid deployment of machine learning models and supporting components by chaining coding agents to cloud automation tools.
• Automation concepts from code AI apply to both traditional code file workflows and Jupyter Notebooks, though integration with notebooks remains less seamless.
• An iterative approach using sidecar Python files combined with custom instructions helps agents access necessary background and context for ML projects.

Workflow Strategies for Machine Learning Engineers

• To leverage code AI agents in machine learning tasks, engineers can provide data samples and visualizations to agents through Python files or prompt contexts.
• Agents can guide creation and comparison of multiple model architectures, metrics, and loss functions, improving efficiency and broadening solution exploration.
• While Jupyter Lab plugin integration is currently limited, some success can be achieved by working with notebook files via code AI tools in standard code editors or by moving between notebooks and Python files for maximum flexibility.