Meetup #29

Scaling Machine Learning Capabilities in Large Organizations

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 in a value-generating application. Combining a serving platform with cultural shifts such as a shift-left approach enhances efficiency even further.


- Unorganized growth of ML capabilities brings several problems regarding maintainability, compliance, and efficiency - Generalize engineering problems and solve them centrally -> create a centralized, self-service model serving platform for company-wide use - Applying shift-left principles mitigates many of the problems that arise when growing your ML capabilities

In this episode

Bertjan  Broeksema

Bertjan Broeksema

Senior Data Engineer , BigData Republic

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



Axel  Goblet

Axel Goblet

Machine Learning Engineer , BigData Republic

Axel has a background in data science. While getting his data science master 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 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.


Demetrios Brinkmann

Demetrios Brinkmann


Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.