“So you’ve created an ML model, but what next?” MLOps in a nutshell

Professional practice shows that the work of a person dealing with machine learning (ML) does not end with creating a model. So what else needs to be done and what is “Ops” all about? These and many other questions were answered during an open meeting by Jacek Jankowiak, one of the founders of the SRG Data Science, currently working as a Data Science and Engineering Consultant.

During the presentation, it was possible to learn more about the life cycle of a machine learning model in business, understand ML models in enterprises and methods that have gained particular popularity in professional practice. Students were able to learn how to explain the sometimes complex details of machine learning to non-technical people so that everyone in the enterprise could benefit from the solutions. Jacek, based on his own experiences, also shared advice on effective work as a Data Scientist and what to do to be appreciated.

Nowadays, when technologies such as machine learning and artificial intelligence are revolutionizing the way business is conducted, it is important that students understand how these technologies are used in practice. By learning about the lifecycle of an ML model, students could better understand how these tools can be used to analyze data, predict market trends, and automate business processes.

Learning the methods that have gained popularity in professional practice allows students to better understand current trends in the industry and prepare for their future work. Knowledge about the latest techniques and tools is invaluable in the labor market, where competition is becoming more and more intense. Additionally, understanding the role of a data scientist in an enterprise is crucial for students who may be considering a career in this field. Knowledge about how to work effectively in this role and what to do to be appreciated in the company is extremely valuable. This may include project management skills, communication with other departments, and ways of presenting analysis results.

The meeting took place on January 16, 2024. The organizer of the meeting is the SRG Data Science.