The Conference for Machine Learning Innovation

Dockerize, Standardize, Deploy and Scale your own Machine Learning Model

Session
Join the ML Revolution!
Until Conference starts:
✓Special discount for Freelancers
✓10% Team Discount
Register Now
Join the ML Revolution!
Until Conference starts:
✓Special discount for Freelancers
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
Register Now
Infos
Wednesday, December 11 2019
14:15 - 15:00
Room:
Saal A+B

Development of machine learning models happens mostly in Jupyter Notebooks these days. To bring them into production, Data scientists struggle sometimes. In this talk, I will show how to transfer your great Jupyter notebook into a docker image that allows you to train your model locally. This local model will also let you predict locally as a service. The nice benefit of this docker image is its scalability: You can, for instance, upload it to AWS Sagemaker and run it on any instance type you want. I will also show you how to implement a prediction endpoint as a callable API.

Behind the Tracks