Production ML pipelines, model serving, monitoring, CI/CD for ML, and taking models from notebook to production.
- Coursera: MLOps Specialization (DeepLearning.AI) - Andrew Ng's comprehensive MLOps specialization.
Intermediate - Made With ML: MLOps Course - Comprehensive free guide to building production ML systems.
Intermediate - Full Stack Deep Learning - From training models to deploying and monitoring in production.
Intermediate - Google Cloud: MLOps Learning Path - Free path covering ML pipeline automation and deployment.
Intermediate - Microsoft: MLOps with Azure ML - Free learning path on MLOps with Azure.
Intermediate
- MLOps Guide by Chip Huyen - Comprehensive guide and curated resources on MLOps practices.
Intermediate - Designing ML Systems (Chip Huyen) — Resources - Free companion resources for the popular ML systems design book.
Intermediate - Reliable Machine Learning (O'Reilly) - Resources on building reliable ML systems.
Advanced
- MLflow Documentation - Open-source platform for the complete ML lifecycle.
Intermediate - BentoML Documentation - Framework for serving, managing, and deploying ML models.
Intermediate - Kubeflow Documentation - ML toolkit for Kubernetes with pipelines and model serving.
Advanced - Seldon Core Documentation - Open-source platform for deploying ML models on Kubernetes.
Advanced - Great Expectations - Data quality and validation for ML pipelines.
Intermediate - Evidently AI - Open-source ML monitoring and observability tool.
Intermediate