Speaker "Chanchal Chatterjee" Details Back



From Concept to Production: Template for the Entire ML Journey


From Concept to Production: Template for the Entire ML Journey
We created a template in python for the entire ML journey from concept to production. The workshop offers a 2 part hands-on tutorial. Each part will be for 4 hours.
Part 1 starts with an example use case. It builds the ML components such as data prep, model hyper train, model train, model deploy and online/batch predict. These components are unit tested in a python notebook. 
Part 2 will show how to deploy these components in a Kubeflow pipeline with orchestration for training and prediction. The entire end to end ML pipeline is now ready for deployment.
At the end of this tutorial you will have hands-on experience building a model from concept to a final production-ready ML pipeline. The tutorial will be implemented on the Google Cloud Platform. Models can include xgboost, tensorflow and scikit learn models.


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 also has a book titled Adaptive ML algorithms with Python. Chanchal received M.S. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University.