mlops best practices
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A Better ML Training Workflow: Stop Training and Praying
Fix your ML training workflow with the Pre-Flight, In-Flight, Post-Flight framework—catch bugs early, monitor runs, and evaluate beyond vanity metrics.
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Agentic MLOps: AI Agents Automating Your ML Stack
Explore how agentic MLOps uses AI agents and MCP to autonomously handle data drift, model deployment, and ML pipeline orchestration across your entire stack.
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ML Experiment Tracking as First-Class Infrastructure
Treating ML experiments as disposable runs undermines reproducibility and model quality.
projects
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clearml-mcp
A Model Context Protocol server that lets AI assistants query ClearML experiments, models and projects
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modalkit
A Python framework for deploying ML models on Modal from YAML config, with auth, queues, batching and storage mounts
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cookiecutter-modern-ml
A project template for NLP, speech or vision models, with uv, typed config, local or SkyPilot training and LitServe serving
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tracelet
Captures PyTorch training metrics and sends them to MLflow, Weights & Biases, ClearML or AIM with little code change