trellixvulnteam / mlflow_python_s461

Repository for ML Flow with Python

ML Flow Examples in Python

This contains a repository of all the MLFlow examples in Python. The project structure currently contains:

1. Basic ML Flow - checking ML flow version
2. Basic Metric Logging
3. Logging a Sci-Kit Learn project, alongide model registration with log_model
4. Conda packaging - deploy your model in a package and pass to other users, or deploy in the cloud with Azure or GCP
5. Specifying additional pip installs - this shows how to build additional requirements into your script
6. Working with PyTorch - to create, and package, a MNIST computer vision classification algorithm and s
7. Working with Tensorflow 2 model and packaging it up to work with MLFlow
8. XGBoost native and XGBoost Scikit Learn - shows different approachesd to packaging these models up
9. Dockerising MLFlow - uses MLFlow guides to show how the model can be packages up into Docker and ran as a microservice.
10. FastAI Example with MLFlow - how to work with FastAI
11. Experiment tracking - shows how to track experiments in MLFlow
12. Multiple workstep example in MLFlow - how to work with multiple workflow steps, as you may want to log metrics from data prep, training and evaluation phases.
13. Hyperparameter tuning and MLFlow - this shows how to log multiple runs when capturing the hyperparameters.
14. • Model Registration - shows how to register a model using a run ID, experiement ID or in script.
15. • Organising runs into experiments
16. • Command Line Interface (CLI) for GCP - aids for working with the command line interface in Google Cloud Platform. To the left there is a menu indicating other cloud deployment options and how to work with MLFlow.

Still to come

I aim to add a couple more modules on model registration, experiment creation and doing all the steps in script. Watch this space for future additions to this repository.