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

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Rui Quintino

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

Technology

Rui Quintino
AI & Analytics Strategy Advisor
DevScope
20210219
“Houston,
we have a model...”
Introduction to MLOps

We have a model... It’s great!
YOU
ARE
HERE

Easy right?
https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning

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

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

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...)

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

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