Robert Risch -DevOps for Machine Learning

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Introduction to MLOps
MLOps, short for Machine Learning Operations, is
the practice of applying DevOps principles to the
machine learning model lifecycle. It aims to
streamline the process of building, deploying,
and monitoring machine learning models in
production.
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Challenges in Machine Learning Deployments
Data Drift
Model Explainability
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Real-world data can change over time, causing
model performance to degrade. Monitoring data
quality is crucial.
Understanding how a model arrives at its
predictions is important for compliance and trust.
Model Versioning
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Keeping track of model versions and
configurations is essential for reproducibility
and rollbacks.
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The Role of DevOps in ML Lifecycle
Continuous Integration
Deployment Automation
Monitoring and Observability
Automating the build, test, and integration of
machine learning pipelines.
Streamlining the deployment of models to
production environments.
Tracking model performance, data quality, and
other key metrics in production.
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Continuous Integration and Deployment for ML
Model Training
Model Deployment
Train machine learning models using the latest
data and code.
Safely deploy the validated model to a production
environment.
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Model Validation
Automatically test the model's performance on
held-out data.
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Monitoring and Observability for ML Models
Data Quality
Model Performance
Model Explainability
Model Lineage
Monitor data drift and distribution changes.
Maintain a history of model versions and
configurations.
Track key metrics like accuracy, precision, and
recall.
Understand how the model is making predictions.