MLflow
ML experiment & model registry
1 case studies
3 specialists
Data Dev Framework ML experiment & model registry
MLflow is used to track experiments, package models, manage model versions in a registry, and deploy models to production across the full ML lifecycle. Its LLM tracking features log prompts, responses, and evaluation metrics, making it useful for both traditional ML and LLM-based applications.
Install with `pip install mlflow` and start the tracking server with `mlflow ui` for a local dashboard. Use `mlflow.start_run()` and `mlflow.log_param()` / `mlflow.log_metric()` in your training scripts. For team use, deploy the tracking server to a shared host and point clients to it via MLFLOW_TRACKING_URI.
$ pip install mlflow` and start the tracking server with `mlflow ui` for a local dashboard Case studies
Series C fintech, ML platform
A fintech ML team was deploying models twice per quarter due to a manual, fragile deployment process. Every deployment required a war room, and 14 production incidents per quarter were traced to model issues.
Migrated 3 years of model history to MLflow with full lineage tracking. Built automated evaluation gates: models must pass 15 quality checks in MLflow before promotion. Rollback to any prior model version in under 5 minutes.
Deployment frequency: 2x/quarter → 8x/week. Production incidents: 14/quarter → 1/quarter. Mean time to recover from model failures: 4 hours → 12 minutes.
Thought leaders
Follow for insights, tutorials, and thought leadership
Weights & Biases / CoreWeave
CEO and co-founder of Weights & Biases, the MLOps platform used by organizations like OpenAI, Salesforce, and Microsoft. Co-founded W&B with Chris Van Pelt and Shawn Lewis in 2017. What began with experiment tracking at OpenAI grew into an end-to-end MLOps platform used by millions. W&B was acquired by CoreWeave in March 2025 for $1.7B.
Databricks
Co-founder and CTO of Databricks and professor at UC Berkeley. Created Apache Spark and MLflow, two of the most influential open-source projects in data engineering and MLOps. MLflow has millions of downloads.
Independent (ex-NVIDIA, Snorkel AI, Netflix)
Author of 'AI Engineering' (2025, most-read book on O'Reilly since launch) and 'Designing Machine Learning Systems' (Amazon #1 bestseller, translated into 10+ languages). Previously worked on ML tooling at NVIDIA (core dev of NeMo), Snorkel AI, and Netflix. Taught ML Systems at Stanford. Founded and sold an AI infrastructure startup.
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