Meetup #116

MLOps in Practice: Common Challenges and Lessons Learned

The different practices and approaches that would ensure the success of your data products, distilled from doing MLOps at different clients from different industries and different levels of maturity:  - It's much more than technical stuff  - The "best" tool is not always the best solution  - Integrating MLOps in system and infrastructure with different levels of maturity

In this episode

Marouen Hizaoui

Marouen Hizaoui

Senior Consultant, Machine Learning Reply

Graduating in 2017 with a degree in Software engineering specializing in intelligent and decision support systems, Marouen has worked for different companies in different industries getting involved in projects of different maturities and different scopes with them being data products as the common denominator. Marouen experienced the need for MLOps firsthand before the concept became mainstream, and he has even started applying the different principles in a few of the projects that he has been involved in from a need basis. Marouen joined ML Reply in mid-2021 and has been working on supporting their different clients through their MLOPS journey as well as raising their company's collective knowledge on MLOps ever since.

LinkedIn

Mo Basirati

Mo Basirati

Senior Consultant/MLOps Lead, Machine Learning Reply

Graduating in 2017 with a degree in Software engineering specializing in intelligent and decision support systems, Marouen has worked for different companies in different industries getting involved in projects of different maturities and different scopes with them being data products as the common denominator. Marouen experienced the need for MLOps firsthand before the concept became mainstream, and he has even started applying the different principles in a few of the projects that he has been involved in from a need basis. Marouen joined ML Reply in mid-2021 and has been working on supporting their different clients through their MLOPS journey as well as raising their company's collective knowledge on MLOps ever since.

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

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.

Ben Epstein

Ben Epstein

Host

Ben was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now a founding software engineer at Galileo (rungalileo.io) focused on building data discovery and data quality tooling for machine learning teams. Ben also works as an adjunct professor at Washington University in St. Louis teaching concepts in cloud computing and big data analytics.