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You will learn to:
Track machine learning experiments effectively.
Analyze and compare experiments for optimal results.
Package and version models for reproducibility.
Effortlessly deploy ML models as REST APIs.
Skills
Machine Learning
MLOps
Model Deployment
Prerequisites
Good understanding of Python
Basic understanding of machine learning fundamentals
Technologies
Python
MLflow
Project Description
In this project, we will dive into MLflow, an open-source platform for tracking and managing machine learning experiments. MLflow provides a comprehensive suite of tools to streamline the ML development process, including experiment tracking, model packaging, and deployment. By using MLflow, we’ll learn to effectively organize, compare, and reproduce machine learning experiments.
We will learn to instrument our machine learning code with MLflow to track parameters, metrics, and artifacts during the training process. We will also learn to use the MLflow UI to visualize and compare the results of different experiments. Finally, we will learn how to package and deploy our models using MLflow.
Project Tasks
1
Initial Setup
Task 0: Get Started
2
Experiment Tracking
Task 1: Create an MLflow Experiment
Task 2: Log Parameters, Metrics, and Artifacts
Task 3: Visualize Experiment Results
Task 4: Compare Experiments and Models
3
Model Packaging
Task 5: Save and Log Models
Task 6: Version and Manage Models
4
Model Deployment
Task 7: Use MLflow Model for Batch Inference
Task 8: Deploy MLflow Model for Real-Time Inference
5
Advanced Features
Task 9: Use Nested MLflow Runs
Task 10: Use MLflow Projects
6
Conclusion
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.