This course will help you and your team understand the concepts and best practices needed to scale up your Machine Learning Operations (MLOps) in your machine learning projects. It provides ML project team members with an extensive overview of the challenges and decisions encountered in building professional ML systems. Rather than focusing on evolving tools, the course emphasizes concepts and frameworks that help to share a common understanding of MLOps within ML teams.

The course is structured around the ML Lifecycle, a key perspective on MLOps, from planning a machine learning project to implementing feedback loops after your project is deployed.

This course will provide you with a solid understanding of the key concepts and practices of MLOps, gained from years of experience working with organizations trying to deploy ML applications at scale.

• The four perspectives on MLOps: The ML Lifecycle, the ML Accountabilities, the ML Principles, the appliedAI Project Management Framework
• Planning an MLOps product
• Improving MLOps processes along the ML Lifecycle, from planning to workflow orchestration

Upon completion of the course, we expect you to be able to explain the fundamental principles and frameworks underlying MLOps. You should be able to discern the differences between professional and unprofessional MLOps workflows, thereby empowering you to identify and implement tangible improvements within your own MLOps processes. In particular, upon completion you should be able to:

• Explain the four perspectives on MLOps, namely the ML Lifecycle, the ML Accountabilities, the ML Principles, and the appliedAI Project Management Framework
• Explain the key considerations and best practices necessary for effective project planning in an ML project
• Name and describe specific improvements applicable to each stage of the ML Lifecycle
• Explain the involvement and responsibilities of the various ML Accountabilities throughout the ML Lifecycle

This course targets ML team members who have a basic understanding of MLOps but have yet to establish a functional and professional MLOps workflow. Before starting the course, we expect learners to be acquainted with various MLOps-related concepts, including data engineering, ML modeling, and version control systems.