Slide 1
Slide 2
MLOps CI/CD for Machine Learning SASHA ROSENBAUM
Slide 3
Sasha Rosenbaum Sr. Program Manager @DivineOps
Slide 4
What is MLOps and WHY should you care?
Slide 5
Machine Learning (ML) Is the science of getting computers to act Without being explicitly programmed
Slide 6
Slide 7
Machine Learning vs DevOps Google searches
Slide 8
Python questions on Stack Overflow
Slide 9
OK, but why should YOU care?
Slide 10
10
Slide 11
Data Scientists just want to Data Science
Slide 12
Deep Learning Some ML training algorithms are complex
Slide 13
Deep Learning - Backpropagation Some ML training algorithms are complex
Slide 14
Typical data scientist work environment
Slide 15
We’ve got the notebook into source control!
Slide 16
Programming Algorithm Answers Data
Slide 17
Machine Learning Answers Algorithm Data
Slide 18
Machine Learning Answers Model Data
Slide 19
Machine Learning Answers Model Data
Slide 20
Machine Learning Answers Data Model Predictions Data
Slide 21
How do we put the model in production?
Slide 22
What is an ML model?
Slide 23
Linear Regression – Housing Prices The training finds a and b such that Y = a+bX+ϵ
Slide 24
Deep Learning The input and output may be ത 𝑌ത vectors 𝑋,
Slide 25
Image Classification
Slide 26
ML Model A definition of the mathematical formula with a number of parameters that are learned from the data
Slide 27
Isn’t this just an API endpoint?!
Slide 28
Do models really change that often?
Slide 29
Models must be improved continuously
Slide 30
TensorFlow Extended FBLearner Flow Uber’s Michelangelo Microsoft Aether
Slide 31
But I don’t work at a big company with thousands of ML engineers!
Slide 32
How do we iterate?
Slide 33
Machine Learning Lifecycle
Slide 34
Data Scientist • • • • • • • Quick iteration Versioning Reuse Great tools Ease of management Unlimited scale Eliminating drift DevOps/SRE Friends? • • • • • • • Quick iteration Versioning Reuse Compliance Observability Uptime Updates
Slide 35
MLOps Workflow Collaborate Build app Test app Release app Monitor app App developer Data scientist Model reproducibility Model validation Model deployment Model retraining
Slide 36
MLOps Workflow Collaborate Build app Test app Release app Monitor app App developer Code, dataset, and environment versioning Data scientist Model reproducibility Model validation Model deployment Model retraining
Slide 37
MLOps Workflow Collaborate App developer Build app Test app Release app Monitor app Train model Automated ML ML Pipelines Hyperparameter tuning Data scientist Model reproducibility Model validation Model deployment Model retraining
Slide 38
MLOps Workflow Collaborate App developer Build app Test app Train model Validate model Release app Monitor app Model validation & certification Data scientist Model reproducibility Model validation Model deployment Model retraining
Slide 39
MLOps Workflow Collaborate App developer Build app Test app Release app Train model Validate model Deploy model Monitor app Model packaging Simple deployment Data scientist Model reproducibility Model validation Model deployment Model retraining
Slide 40
MLOps Workflow Collaborate App developer Build app Test app Release app Monitor app Train model Validate model Deploy model Monitor model Model management & monitoring Retrain model Data scientist Model performance analysis Model reproducibility Model validation Model deployment Model retraining
Slide 41
Build Your Own MLOps Platform + +
Slide 42
ML Pipeline A reusable, scaleable ML workflow template Kubeflow pipeline A reusable, scalable ML workflow template that runs on containers
Slide 43
Azure ML • • • • • Prep data Train Test Deploy Manage
Slide 44
The CI/CD Pipeline
Slide 45
GitHub Repo https://github.com/DivineOps/kubeflow-and-mlops
Slide 46
Azure Resources
Slide 47
The Code
Slide 48
Build Pipeline https://github.com/DivineOps/kubeflow-and-mlops
Slide 49
Kubeflow Pipeline https://www.kubeflow.org/docs/azure/azureendtoend/
Slide 50
Release Pipeline https://dev.azure.com/sasrose/kubeflow/_release?_a=releases&view=mine&definitionId=3
Slide 51
Even a simple CI/CD pipeline is better than none!
Slide 52
AI Ethics
Slide 53
Bias is a property of information
Slide 54
Build AI responsibly!
Slide 55
Thank You! @DivineOps
Slide 56
Resources GitHub repo https://www.kubeflow.org/docs/azure/azureendtoend/ Deploy Kubeflow on Azure https://www.kubeflow.org/docs/azure/deploy/install-kubeflow/ Example Kubeflow Azure Pipeline https://www.kubeflow.org/docs/azure/azureendtoend/ Release pipeline https://dev.azure.com/sasrose/kubeflow/_release
