Topic guide

MLOps and production ML

Pipelines, monitoring, deployment, experiment tracking, and APIs for maintainable machine learning systems.

For teams with models or prototypes that need to become reliable, observable, and deployable.

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MLOps

New

Orchestrate ML Pipelines with Prefect

Jun 2026

Go from fragile training scripts to robust, observable ML workflows using Prefect. Schedule retraining, handle failures gracefully, and monitor every run.

ML Model Monitoring in Production with Evidently

Jun 2026

Learn how to detect data drift and model degradation in production using Evidently AI. Build automated reports and alerts before your model silently fails.

Evidently

Python

MLOps

Serve a Machine Learning Model with Sklearn, FastAPI and Docker

Mar 2026

Wrap a trained scikit-learn model in a FastAPI REST endpoint, containerize it with Docker, and serve predictions at scale — a complete MLOps deployment template with a working GitHub repository.

Manage Your Machine Learning Lifecycle with MLflow in Python

Feb 2026

Use MLflow to track experiments, compare runs, version models in the Model Registry, and promote the best model to production — all without losing reproducibility.

MLflow

Python

MLOps

Rodrigo Arenas

MLOps and production ML

Orchestrate ML Pipelines with Prefect

ML Model Monitoring in Production with Evidently

Serve a Machine Learning Model with Sklearn, FastAPI and Docker

Manage Your Machine Learning Lifecycle with MLflow in Python

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