Getting Started with MLOps

“MLOps is the integration of machine learning (ML) with DevOps focusing on streamlining the end-to-end machine learning life cycle. It emphasizes collaboration automation and reproducibility to deliver reliable and scalable machine learning solutions. By implementing MLOps practices organizations can efficiently manage and govern their machine learning workflows leading to faster development cycles better model performance and enhanced collaboration among data scientists and engineers.

In this course you will delve into the theoretical aspects of MLOps and understand what sets it apart from traditional software development. You will explore the factors that affect ML models in production and gain insights into the challenges and considerations of deploying machine learning solutions.

Next you will see how the Machine Learning Canvas can help you understand the components of ML development. You will then explore the end-to-end machine learning workflow covering stages from data preparation to model deployment.

Finally you will look at the different stages in MLOps maturity in your organization levels 0 1 and 2. You will learn how organizations evolve in their MLOps journey and the key characteristics of each maturity level. “