📦 These 10 Python libraries are not just tools… they are industrial accelerators for the full MLOps lifecycle:
- 1️⃣ MLflow → experiment management
- 2️⃣ DVC → data versioning
- 3️⃣ Kubeflow → Kubernetes orchestration
- 4️⃣ Prefect → painless pipelines
- 5️⃣ FastAPI → deployment as a service
- 6️⃣ Evidently → monitoring and drift detection
- 7️⃣ Weights & Biases → collaboration and optimization
- 8️⃣ Great Expectations → data validation
- 9️⃣ BentoML → packaging and cross-platform deployment
- 🔟 Optuna → automatic hyperparameter tuning
💡 The difference between a model that works and one that makes an impact is in the stack that supports it.
More information at the link 👇
Also published on LinkedIn.
