What MLOps Is, Why It Matters, and How to Learn It

“What”

MLOps is a culture and set of processes that aim to improve the collaboration between data scientists and software engineers, and enable continuous integration and delivery of machine learning models. It aims at establishing a culture and environment where building, testing, and releasing machine learning models can occur rapidly, frequently, and more reliably.

“Why”

The benefits of MLOps include:

• Increased efficiency: Automating the machine learning lifecycle can help data scientists and software engineers to work more efficiently and avoid potential errors.

• Improved quality: Automating the machine learning lifecycle can help to ensure that models are of high quality and that they meet the requirements of the business.

• Increased transparency: Automating the machine learning lifecycle can help to make the process more transparent and understandable to all stakeholders.

• Increased speed: Automating the machine learning lifecycle can help to speed up the process, enabling faster delivery of models to users.

“How”

Firstly, it is important to have great experience with DevOps practices and principles. Secondly, it is helpful to have experience working with cloud-based platforms and tools. Finally, it is beneficial to have a strong understanding of both machine learning and software development.