Meetup #35

Bring Your On-Prem ML Use Cases to Production on the Google Cloud using Kubeflow

If you are wondering how to bring your on-prem ML workloads to a scalable, portable, composable and secure production platform, the open source Kubeflow pipelines is your answer. We demonstrate an easy step by step process with ML models built with scikit-learn, xgboost and tensorflow ML frameworks. We will show how to create an end to end ML pipeline on the Google Cloud including data prep, hyperparameter tuning, model training, model deployment, prediction, explanation and training orchestration. The solution can be extended to the Anthos framework for a full multi-cloud deployment.

In this episode

Chanchal  Chatterjee

Chanchal Chatterjee

AI Leader, Google

Chanchal Chatterjee, Ph.D, held several leadership roles in machine learning, deep learning and real-time analytics. He is currently leading Machine Learning and Artificial Intelligence at Google Cloud Platform. Previously, he was the Chief Architect of EMC CTO Office where he led end-to-end deep learning and machine learning solutions for data centers, smart buildings and smart manufacturing for leading customers. Chanchal received several awards including Outstanding paper award from IEEE Neural Network Council for adaptive learning algorithms recommended by MIT professor Marvin Minsky. Chanchal founded two tech startups between 2008-2013. Chanchal has 29 granted or pending patents, and over 30 publications. Chanchal received M.S. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University.



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.