Robert Risch -DevOps for Machine Learning.pdf

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.

1. 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.

2. Challenges in Machine Learning
Deployments
1
Data Drift
Real-world data can change over
time, causing model performance to
degrade. Monitoring data quality is
crucial.
2
Model Explainability
Understanding how a model arrives
at its predictions is important for
compliance and trust.
3
Model Versioning
Keeping track of model versions and configurations is essential for reproducibility and
rollbacks.

3. The Role of DevOps in ML Lifecycle
Continuous
Integration
Automating the build, test, and
integration of machine learning
pipelines.
Deployment
Automation
Streamlining the deployment of
models to production
environments.
Monitoring and
Observability
Tracking model performance,
data quality, and other key
metrics in production.

4. Continuous Integration and
Deployment for ML
1
Model Training
Train machine learning models using the latest
data and code.
2
Model Validation
Automatically test the model's performance on
held-out data.
3
Model Deployment
Safely deploy the validated model to a
production environment.

5. Monitoring and Observability for ML
Models
Data Quality
Monitor data drift and
distribution changes.
Model
Performance
Track key metrics like
accuracy, precision,
and recall.
Model
Explainability
Understand how the
model is making
predictions.
Model Lineage
Maintain a history of
model versions and
configurations.