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MLOps with GitHub Actions and Azure Machine Learning Azure ML + GitHub Actions Pulkit Agarwal, Product Manager @ GitHub
MLOps ?
DevOps for ML
What is DevOps? DevOps is the union of people, processes, and technologies to deliver continuous value to users Developmen...
What is MLOps? Union of people, processes, and technologies to deliver continuous value to users using a ML based software...
Is MLOps challenging ? Why ?
Building a model
Building a model Data ingestion Data analysis Data transformation Data validation Data splitting Model validation Training...
Cat or Not ?
Collaboration on code Remote Training Models bookkeeping (Many!) Manual Steps Managing Data and Code Updates Challenges wi...
MLOps sounds hard ?
Lets make MLOps Easy-ish
Lets make MLOps Easy-ish
Collaboration on code Remote Training Models Book keeping (Many!) Manual Steps Data and Code Updates Making MLOps Easy(-is...
Making MLOps Easy(-ish) + + ML Optimized Compute Source Control ML Aware CI/CD
Making MLOps Easy(-ish) + + Source Control ML Aware CI/CD
Making MLOps Easy(-ish) + + ML Aware CI/CD
Making MLOps Easy(-ish) + +
DEMO aka.ms/aidevday-mlops
Thank You Happy to take any questions ! GitHub Handle: @pulkitaggarwl || Email ID: puagarw@microsoft.com
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Ml ops with azure ml & git hub actions

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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.

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Ml ops with azure ml & git hub actions

  1. 1. MLOps with GitHub Actions and Azure Machine Learning Azure ML + GitHub Actions Pulkit Agarwal, Product Manager @ GitHub
  2. 2. MLOps ?
  3. 3. DevOps for ML
  4. 4. What is DevOps? DevOps is the union of people, processes, and technologies to deliver continuous value to users Development + operations
  5. 5. What is MLOps? Union of people, processes, and technologies to deliver continuous value to users using a ML based software application
  6. 6. Is MLOps challenging ? Why ?
  7. 7. Building a model
  8. 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. 9. Cat or Not ?
  10. 10. Collaboration on code Remote Training Models bookkeeping (Many!) Manual Steps Managing Data and Code Updates Challenges with MLOps
  11. 11. MLOps sounds hard ?
  12. 12. Lets make MLOps Easy-ish
  13. 13. Lets make MLOps Easy-ish
  14. 14. Collaboration on code Remote Training Models Book keeping (Many!) Manual Steps Data and Code Updates Making MLOps Easy(-ish)
  15. 15. Making MLOps Easy(-ish) + + ML Optimized Compute Source Control ML Aware CI/CD
  16. 16. Making MLOps Easy(-ish) + + Source Control ML Aware CI/CD
  17. 17. Making MLOps Easy(-ish) + + ML Aware CI/CD
  18. 18. Making MLOps Easy(-ish) + +
  19. 19. DEMO aka.ms/aidevday-mlops
  20. 20. Thank You Happy to take any questions ! GitHub Handle: @pulkitaggarwl || Email ID: puagarw@microsoft.com

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