MLOps empowers data scientists and machine learning engineers to bring together their knowledge and skills to simplify the process of going from model development to release/deployment. This allows practitioners to automate the end to end machine Learning lifecycle to frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services. We will be covering how you can get started with MLOps using GH Actions and Azure ML as building blocks.

1. MLOps with GitHub Actions
and Azure Machine Learning
Azure ML + GitHub Actions
Pulkit Agarwal, Product Manager @ GitHub

2. MLOps ?

3. DevOps for ML

4. What is DevOps?
DevOps is the union of
people, processes, and
technologies
to deliver continuous
value to users
Development + operations

5. What is MLOps?
Union of people, processes, and technologies
to deliver continuous value to users
using a ML based software application

6. Is MLOps challenging ?
Why ?

7. Building a model

8. Building
a model
Data ingestion Data analysis
Data
transformation
Data validation Data splitting
Model
validation
Training
at scale
Logging
Deploying the
model
Monitoring
Machine Learning Applications Lifecycle

9. Cat or Not ?

10. Collaboration
on code
Remote
Training
Models
bookkeeping
(Many!)
Manual Steps
Managing
Data and Code
Updates
Challenges with MLOps

11. MLOps sounds hard ?

12. Lets make MLOps
Easy-ish

13. Lets make MLOps
Easy-ish

14. Collaboration
on code
Remote
Training
Models Book
keeping
(Many!)
Manual Steps
Data and Code
Updates
Making MLOps Easy(-ish)

15. Making MLOps Easy(-ish)
+ +
ML
Optimized
Compute
Source
Control
ML
Aware
CI/CD

16. Making MLOps Easy(-ish)
+ +
Source
Control
ML
Aware
CI/CD

17. Making MLOps Easy(-ish)
+ +
ML
Aware
CI/CD

18. Making MLOps Easy(-ish)
+ +

19. DEMO
aka.ms/aidevday-mlops

20. Thank You
Happy to take any questions !
GitHub Handle: @pulkitaggarwl || Email ID: puagarw@microsoft.com