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Starts 3 June 2025 06:07
Ends 3 June 2025
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Using MLflow and Databricks to Deploy ML Models in Production
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.
Data Science Festival
via YouTube
Data Science Festival
2408 Courses
58 minutes
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Overview
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.
Syllabus
- **Introduction to MLflow and Databricks**
- **MLflow for Model Management**
- **Model Deployment Strategies**
- **Integrating Databricks with MLflow**
- **Databricks Feature Store**
- **AutoML in Databricks**
- **Real-World Application and Case Studies**
- **Conclusion and Future Trends**
Overview of MLflow
Introduction to Databricks platform
Understanding the Production ML Workflow
Installing and Setting up MLflow
Tracking Experiments and Runs
Managing ML Models with MLflow Registry
Batch vs. Real-Time Deployment
Deploying Models with MLflow
Managing Model Versions and Rollbacks
Configuring MLflow with Databricks
Logging and Tracking Experiments in Databricks
Utilizing Databricks Notebooks for ML Experiments
Introduction to Feature Stores
Managing Features in Databricks
Feature Reusability and Sharing
Overview of AutoML
Using Databricks AutoML for Automated Model Training
Evaluating and Deploying AutoML Models
Case Study: End-to-End Model Deployment
Best Practices for ML Deployment
Challenges and Troubleshooting in Production
Future Trends in ML Model Deployment
Advanced Features in Databricks and MLflow
Continuous Learning and Improvement Strategies
Subjects
Data Science