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New course: MLOps

Defining MLOps best practices around data, code and model has become a key success factor for data-driven organizations. On April 7, our course «MLOps» will start. Course instructor Dr. Nitin Kumar has delivered around a dozen end-to-end data products. In this interview he explains what the participants will learn in the course.

Can you briefly explain what MLOps is?

MLOps is defined as a set of practices that aim to deploy and maintain machine learning (ML) models in production reliably and efficiently (Wikipedia). It is often considered as DevOps for Machine Learning and requires combining the power of people, technology, and processes to realize financial benefits using ML.

Can you list a few MLOps use cases?

MLOps is a general framework around requirements and practices that are applied to any machine learning use case. Some commonly known use-cases across industries where knowledge of MLOPs helps include predictive maintenance, churn prediction, customer segmentation, information extraction from documents and many more.

What are the advantages of having MLOps skills?

MLOps skills are a necessity to succeed in the age of artificial intelligence. An understanding of end-to-end MLOps process will help anyone involved in machine learning contribute to the success of such use-cases. A lot of focus has been given to the technology part of MLOps but less so to people and processes. In this course we will define MLOps requirements covering all three aspects People + Processes + Technology.

Who is this course for?

This course is ideal for anyone working or planning to work with machine learning uses-cases e.g. Data Scientists, Data product owners, DevOps, Data Engineers, Software engineers, Managers, ML product end-users, Innovation experts, Compliance and regulatory teams, security experts and more…

What do the participants learn in your course?

The participants will learn about everything that goes into productionization of an ML use-case starting from the ideation stage all the way to the maintenance of such systems which usually requires an intricate involvement of business experts and data professionals. Participants will have the opportunity to define requirements and processes around their real-life ML use-cases.

The course starts on April 7 and takes place in the evenings. Application deadline is March 24. More information and registration: MLOps

Learn more about the wide range of continuing education courses offered by the Institute of Computational Life Sciences in the field of Computational Science and Artificial Intelligence: www.zhaw.ch/icls/continuingeducation