MLOps is used to scale and govern machine learning operations, while AIOps provides a way to solve business problems and reduce incident management overhead using AI. In this course, you'll begin with an introduction to what AIOps is and which common IT operations it can be applied to. Discover AIOps capabilities, main business areas which AIOps can be incorporated into, and the benefits to an organization considering adoption. Next, explore various business use cases suitable for AIOps and adoption from a business and technical perspective. Moving on, look at the capabilities provided by MLOps and the benefits of MLOps to a data scientist. Next, explore the steps involved in implementing MLOps including the tools, challenges, and best practices involved. Upon completion of this course, you'll be able to identify the differences between AIOps, MLOps, and DevOps.

DataOps processes help to manage data analytics and processing. It combines DevOps teams with other data roles to supply processes, tools, and organizational structures to support enterprises. Through this course, learn DataOps guiding principles and how to implement them properly in an organization. Explore DataOps platforms and tools, key DataOps principles, and best practices and challenges in implementing DataOps. Next, learn about the role of DataOps in automating data governance and compare DataOps to DevOps. Finally, discover how to establish a DataOps team and practice, the individual DataOps team roles and responsibilities, and the components of a DataOps architecture. After course completion, you'll be able to outline what DataOps is and how to properly implement it.