Automating the ML Training Lifecycle with Multi-Agent Orchestration
kmeanskaran · x · 2026-08-08
The author explores building multi-agent systems to automate the entire ML lifecycle. By defining sequential subagents and skills for stages like data engineering, feature engineering, model training, evaluation, drift detection, and rollback, developers can leverage observability and orchestration tools to minimize manual intervention. The author plans to extend this proof-of-concept to deployment, noting that edge cases will provide the most valuable insights.
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