MLOps Meetup #29 / Scaling ML Capabilities in Large Organizations // Bertjan Broeksema & Axel Goblet

Machine learning has become an increasingly important means for organizations to extract value from their data. Many companies start off with successful proofs of value but face problems when scaling their capabilities afterward. By generalizing engineering problems and solving them centrally, scaling becomes much more feasible. Model serving platforms generalize the problem of turning a machine learning model into a value-generating application. Combining a serving platform with cultural shifts such as a shift-left approach enhances efficiency even further.

Bertjan is a Senior Data Engineer, with 15 years of experience in the software industry, specializing in data science and engineering for the last 10 years. He built a variety of data products and machine learning platforms. He has worked on both traditional desktop applications as well as cloud-native applications in DevOps teams. He's a craftsman with a passion for delivering value through high-quality software, aligning stakeholders, and coaching junior and medior team members.

Axel has a background in data science. While getting his data science master's degree, he did software engineering and data science projects for a wide range of customers. This experience taught him that the main complexity of data science projects lies in the software built around the predictive models. After finishing his degree, he joined the BigData Republic. Axel currently helps companies bring their data science capabilities to the next level. His main interest lies in tooling that speeds up the development of machine learning applications.

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