MLOps & Deployment

The MLOps & Deployment category focuses on the critical final stage of the machine learning lifecycle: taking models out of the Jupyter notebook and putting them into production. MLOps (Machine Learning Operations) applies the principles of DevOps to machine learning systems, ensuring that models are deployed reliably, monitored continuously, and updated efficiently. The courses curated here will teach you how to build automated CI/CD pipelines for machine learning code and data. You will learn how to containerize models using Docker, deploy them using Kubernetes, and serve predictions via APIs. Furthermore, these resources cover strategies for monitoring model drift, managing model versions, and ensuring the long-term performance of AI systems in real-world environments. MLOps is the key to turning experimental data science into tangible business value. Learn how to operationalize AI and ensure your models deliver consistent impact.