Robert Risch -DevOps for Machine Learning
                     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.
Challenges in Machine Learning 
Deployments
1 Data Drift 2 Model Explainability
Real-world data can change over Understanding how a model arrives 
time, causing model performance to at its predictions is important for 
degrade. Monitoring data quality is compliance and trust.
crucial.
3 Model Versioning
Keeping track of model versions and configurations is essential for reproducibility and 
rollbacks.
The Role of DevOps  in ML L ifecycle
Continuous  Deployment Monitoring and 
Integration Automation Observability
Automating the build, test, and Streamlining the deployment of Tracking model performance, 
integration of machine learning models to production data quality, and other key 
pipelines. environments. metrics in production.
Continuous Integration and 
Deployment for ML
Model Training Model Deployment
Train machine learning models using the latest Safely deploy the validated model to a 
data and code. production environment.
1 2 3
Model Validation
Automatically test the model's performance on 
held-out data.
Monitoring and Observability for ML 
Models
Data Quality Model Model Model Lineage
Monitor data drift and Performance Explainability Maintain a history of 
distribution changes. Track key metrics like Understand how the model versions and 
accuracy, precis ion, model is making configurations.
and recall. predictions.