MLOps Concepts

This course typically takes learners two hours to complete, though how you pace your learning is completely up to you.

This course is ideal for data scientists, engineers, leaders, and anyone interested in understanding how to implement machine learning continuously, reliably, and efficiently.

We recommend you complete our Understanding Machine Learning and Understanding Data Engineering courses if you are unfamiliar with basic concepts related to these fields.

MLOps, short for Machine Learning Operations, is a set of practices, principles, and tools that unifies machine learning (ML) system development and operations (Ops). It aims to automate and streamline the end-to-end ML lifecycle, from integrating ML models into production environments to monitoring their performance. MLOps ensures that ML models are developed, deployed, and maintained efficiently, reliably, and continuously, similar to the DevOps approach in software development. In this course, you'll delve deep into the core concepts of MLOps and learn how it can be applied to scale and automate ML projects effectively.

MLOps provides answers to fundamental questions about scaling and automating machine learning projects. It helps in reducing manual intervention, improving model performance, and ensuring that machine learning models generate real business value.