This course empowers the students to be more efficient, effective, and productive in modern, real-world ML projects by adopting best practices around reproducible workflows. In particular, it teaches the fundamentals of MLops and how to: a) create a clean, organized, reproducible, end-to-end machine learning pipeline from scratch using MLflow b) clean and validate the data using pytest c) track experiments, code, and results using GitHub and Weights & Biases d) select the best-performing model for production and e) deploy a model using MLflow. Along the way, it also touches on other technologies like Kubernetes, Kubeflow, and Great Expectations and how they relate to the content of the class.

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To answer the basic question of "What is MLOps?" we need to understand first that what is DevOps. DevOps is a set of practices that combines software development and IT operations. It aims to shorten the systems development life cycle and provide continuous delivery with high software quality. DevOps is complementary with Agile software development; several DevOps aspects came from Agile methodology. DevOps is the offspring of agile software development – born from the need to keep up with the increased software velocity and throughput agile methods have achieved.

As organizations add machine learning (ML) to their workflows, it's tempting to try to squeeze model creation and deployment into the existing software development lifecycle (SDLC). However, ML is fundamentally different than traditional applications, and it's important to account for that in a new, unique process called the machine learning development lifecycle. We have identified five challenges every organization should keep in mind as they begin to support ML development. Machine learning is successful when the right tool is selected for a given job. Depending on the use case, a data scientist might choose Python, R, Scala or another language to build one model, and another language for a second model.