The Conference for Machine Learning Innovation

MLOps: DevOps for Machine Learning

Session
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:

✓ Save up to €494
✓ 10% Team Discount✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:

✓ Save up to €494
✓ 10% Team Discount✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos

In recent years, we have gained an essential insight about the field of software development: DevOps is no longer a nice to have – it is absolutely necessary. A fast pipeline of Continuous Integration, Continuous Delivery, and Continuous Deployment delivers value to customers. Delivering value and solving problems is also the goal of every machine learning model. However, building the model is the easy part. The real challenge is to build an integrated machine learning system.

You’ll leave this talk with an understanding of how we can apply learnings from "traditional" software engineering in a data science environment. You’ll learn how we can version, test, and monitor our model, our data, and all the other moving parts of our ML system. We will talk about different degrees of maturity in MLOps, the big picture of pipeline architectures, and the nitty-gritty details about why you don’t want to deploy your Jupyter notebooks to production. Also, we will explore weapons of choice for different parts of your ML system and common misconceptions about machine learning in production.

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Singapore Singapore .

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Berlin Berlin .

This Session originates from the archive of Diese Session stammt aus dem Archiv von MunichMunich . Take me to the program of . Hier geht es zum aktuellen Programm von Munich Munich .

This Session Diese Session originates from the archive of stammt aus dem Archiv von MunichMunich . Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

Behind the Tracks