Robert Risch -DevOps for Machine Learning

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 Data Drift Model Explainability 1 2 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 3 Keeping track of model versions and configurations is essential for reproducibility and rollbacks.

3. 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.

4. 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. 1 2 3 Model Validation Automatically test the model's performance on held-out data.

5. 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.