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“Houston, we have a model...” Introduction to MLOps

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Rui Quintino
Rui Quintino

“Houston, we have a model...” Introduction to MLOps Session at FEUP by DevScope

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Rui Quintino AI & Analytics Strategy Advisor DevScope 20210219 “Houston, we have a model...” Introduction to MLOps
devscope.net
We have a model... It’s great! YOU ARE HERE
Now what?
We just deploy it!
Easy right? https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
What about the data Pipeline/preproc?
Or Data Drift -> causing Model Performance degradation? https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb
Plus... • How/who can we retrain if needed (ex: data drift)? • What are the software requirements? • How can we later update lib/os versions? (ex: security) • How can we monitor model performance in production? • How do we ensure data checks in production? • How was the model trained? What was the performance? • How to scale? And Reproduce? • ...
Model & ML code just tip of the iceberg...
Model & ML code just tip of the iceberg... https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
Introducing MLOps – Machine Learning & Operations
DevOps MLOps Code testing Code reproducibility App deployment Model retraining Model validation Model reproducibility Model deployment
Build app Collaborate Test app Deploy app Monitor app App developer
Model reproducibility Model retraining Model deployment Model validation Train model Validate model Deploy model Monitor model Retrain model Build app Collaborate Test app Release app Monitor app Audit /interpretability App developer Data scientist
GitHub & Azure ML MLOps Demo
Lets try a new hyper param configuration...
Compute, Experiment, Model, Deployment in Azure ML
Tracking critical for MLOps, but also for Experimenting
With GitHub & CML • Automation for Experimentation • Ex: Smoke Test Locally (Fast) • Queue Reproducible in Github • Using notebooks Experimentation
GitHub Workflows to support Experiment Queues Check Continuous ML (CML) framework in Github
Branch for new experiment, change code, queue in GitHub
Commit & Push branch to queue workflow in GitHub
Workflow running...
Notebook execution using papermill on Github agent
Train performance results on commits (also PRs)
MLOps Recap •From Models to Pipelines •Execution of Pipelines produce models •Train for Deployment is fully automated/reproducible •Pipelines enable auto model retraining •Automation, Automation, Automation... •Monitoring for Data Drift, Concept Drift, Model Drift •Tracking, Metadata & Artifact Stores
Mature MLOps Example (Level 1) https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
Not best fit for interative/model development
Final notes •MLOps doesn’t guarantee ML success •MLOps should improve feedback and learning (not block DataScientists from business/data understanding) •Dont reinvent the wheel... •Still a young field Dont believe everything you read in docs (and ppt...) •When/If to introduce MLOps (it depends...)
MLOps References
https://mlops.githubapp.com/
58
Also • https://azure.microsoft.com/en-us/services/machine- learning/mlops/ • https://cloud.google.com/solutions/machine-learning/mlops- continuous-delivery-and-automation-pipelines-in-machine- learning • https://github.com/microsoft/MLOps • https://databricks.com/blog/2020/10/13/using-mlops-with- mlflow-and-azure.html • https://github.com/Azure/MachineLearningNotebooks
devscope.net Thank you!
© 2016 DevScope. All rights reserved. Rua Passos Manuel Nº 223 – 4º Andar 4000-385 Porto T. +351 223 751 350/51 F. +351 223 751 352 Av. Sidónio Pais, Nº 2 – 3º Andar 1050-214 5 Lisboa info@devscope.net www.devscope.net
Helping customers succeed
“Houston, we have a model...” Introduction to MLOps

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