Using MLflow and Databricks to Deploy ML Models in Production

Übersicht

Explore MLflow and Databricks for production ML model deployment. Learn end-to-end management, model tracking, and deployment strategies, plus Databricks' Feature Store and AutoML.

Lehrplan

• **Introduction to MLflow and Databricks**
Overview of MLflow
Introduction to Databricks platform
Understanding the Production ML Workflow
• **MLflow for Model Management**
Installing and Setting up MLflow
Tracking Experiments and Runs
Managing ML Models with MLflow Registry
• **Model Deployment Strategies**
Batch vs. Real-Time Deployment
Deploying Models with MLflow
Managing Model Versions and Rollbacks
• **Integrating Databricks with MLflow**
Configuring MLflow with Databricks
Logging and Tracking Experiments in Databricks
Utilizing Databricks Notebooks for ML Experiments
• **Databricks Feature Store**
Introduction to Feature Stores
Managing Features in Databricks
Feature Reusability and Sharing
• **AutoML in Databricks**
Overview of AutoML
Using Databricks AutoML for Automated Model Training
Evaluating and Deploying AutoML Models
• **Real-World Application and Case Studies**
Case Study: End-to-End Model Deployment
Best Practices for ML Deployment
Challenges and Troubleshooting in Production
• **Conclusion and Future Trends**
Future Trends in ML Model Deployment
Advanced Features in Databricks and MLflow
Continuous Learning and Improvement Strategies

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Fachgebiete

Data Science