MLOps Concepts

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

Understanding Machine Learning Operations (MLOps) is essential for any data scientist, engineer, or leader to take machine learning models from a local notebook to a functioning model in production. This course introduces you to the key processes, phases, and levels of MLOps, including design, development, deployment, and monitoring. You'll discover how automation enables organizations to efficiently launch, monitor, and update their machine learning models.

• Introduction to MLOps
• First, you’ll learn about the core features of MLOps. You’ll explore the machine learning lifecycle, its phases, and the roles associated with MLOps processes.
• Design and Development
• Next, you’ll learn about the design and development phase in the machine learning lifecycle. You’ll explore added value estimation, data quality, feature stores, and experiment tracking.
• Deploying Machine Learning into Production
• In this chapter, you’ll dive into the concepts relevant to deploying machine learning into production, such as runtime environments, containerization, CI/CD pipelines, and deployment strategies.
• Maintaining Machine Learning in Production
• Finally, you’ll learn about maintaining machine learning in production, with concepts such as statistical and computational monitoring, retraining, different levels of MLOps maturity, and tools that can be used within the machine learning lifecycle to simplify processes.