Managing the Complete Machine Learning Lifecycle with MLflow

Part 3 of 3

Other parts:

• Part 1
• Part 2
• Watch workshops on YouTube

Content for the MLflow Series

Machine Learning (ML) development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models.

To solve these challenges, MLflow, an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, encapsulate models that can be used with many existing tools, and central respositry to share models, accelerating the ML lifecycle for organizations of any size.

Goal and Objective

Aimed at beginner or intermediate level, this three-part series aims to educate data scientists or ML developer in how you leverage MLflow as a platform to track experiments, package projects to reproduce runs, use model flavors to deploy in diverse environments, and manage models in a central respository for sharing.

What you will learn

Understand the four main components of open source MLflow——MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry—and how each compopnent helps address challenges of the ML lifecycle.

• How to use MLflow Tracking to record and query experiments: code, data, config, and results.
• How to use MLflow Projects packaging format to reproduce runs
• How to use MLflow Models general format to send models to diverse deployment tools.
• How to use Model Registry for collaborative model lifecycle management
• How to use MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics

Instructor

• Jules S. Damji @2twitme

MLflow workshop part 3

In this part 3, we will cover:

• Concepts and motivation behind Model Registry
• Model Registry Workflows
  • Model Registry UI
  • Model Registry API
• Managed MLflow Model Registry Demo
• Use Model Registry UI on the local host
• Deploy and serve a model from the Model Registry on the local host

Prerequisites

• Part 1 and Part 2 of this series
• Knowledge of Python 3 and programming in general
• Preferably a UNIX-based, fully-charged laptop with 8-16 GB, with a Chrome or Firefox browser
• Familiarity with GitHub, git, and an account on Github
• Some knowledge of Machine Learning concepts, libraries, and frameworks
  • scikit-Learn
  • pandas and Numpy
  • matplotlib
  • keras/TensorFlow
• PyCharm/IntelliJ or your choice of syntax-based Python editor
• pip/pip3 or conda and Python 3 installed
• Knowledge on how to use conda or create pipenv enviroments
• Loads of virtual laughter, curiosity, and a sense of humor ... :-)

Obtaining the Tutorial Material

Familiarity with git is important so that you can get all the material easily during the tutorial and workshop as well as continue to work on in your free time, after the session is over.

git clone [email protected]:dmatrix/mlflow-workshop-part-3.git or git clone https://github.com/dmatrix/mlflow-workshop-part-3.git

Documentation Resources

This tutorial will refer to documentation:

1. MLflow
2. Numpy
3. Pandas
4. Scikit-Learn
5. Keras
6. TensorFlow
7. Matplotlib

Installation and Setup environment

1. git clone [email protected]:dmatrix/mlflow-workshop-part-3.git or git clone https://github.com/dmatrix/mlflow-workshop-part-3.git
2. cd <your_cloned_directory>/mlflow-workshop-part-3
3. Install MLflow and the required Python modules within your conda activated environment or pipenv environment if using it
   • pip install -r req.txt or pip3 install -r req.txt
4. cd src
5. If using PyCharm or IntelliJ, create a project and load source files in into your project or use your choice of syntax-highlighted editor, like Sublime or vim
6. How to use PyCharm and MLflow

Let's go!

Session Tutorials

We'll use localhost (or your laptop) to run and register models with Model Registry, followed by deploying a "production" model as a REST endpoint locally.

Tutorial 1 - Part 1

1. python run_weather_forecast.py
2. launch mlflow ui --backend-store-uri sqlite:///mlruns.db
3. Got to http://127.0.0.1:5000
4. Pick the best model, register with Model Registry as PowerForecastingModel
5. Choose second best model and create version 2 in the Model Registry
   • Transition the best model into Production
   • Transition the second best model into Staging

Tutorial 1 - Part 2

Let's take our production model from from our Model Registry and deploy and serve models locally as a REST endpoint to a server launched by MLflow CLI.

1. From the same directory run:

• deploy_model.sh

This launches a gunicorn server serving at the localhost 127.0.0.1:5000. Now you can score locally on the deployed produciton model as a REST point.

2. From another terminal send a POST request with our JSON payload

• make_predictions.sh or python make_predictions.py

Homework/Lab Assignment

• Use Model Registry UI or API to register models from workshop part-1: Petrol consumption

• Consult documentation how to use mflow models predict

• Use mlflow models predict [OPTIONS] to predict

  • For example:

  mlflow models predict -m, --model-uri <model_uri> -i, --input-path <input_path>

Cheers,

Jules