Book Image

Practical Deep Learning at Scale with MLflow

By : Yong Liu
5 (1)
Book Image

Practical Deep Learning at Scale with MLflow

5 (1)
By: Yong Liu

Overview of this book

The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas. From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You’ll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you’ll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox. By the end of this book, you’ll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.
Table of Contents (17 chapters)
1
Section 1 - Deep Learning Challenges and MLflow Prime
4
Section 2 –
Tracking a Deep Learning Pipeline at Scale
7
Section 3 –
Running Deep Learning Pipelines at Scale
10
Section 4 –
Deploying a Deep Learning Pipeline at Scale
13
Section 5 – Deep Learning Model Explainability at Scale

Summary

In this chapter, we took a deep dive into how we can track code and data versions in an MLflow experiment run. We started by reviewing the different types of notebooks: Jupyter notebooks, Databricks notebooks, and VS Code notebooks. We compared them and recommended that VS Code should be used to author a notebook due to its IDE support, as well as its Python styling, autocompletion, and many more rich features.

Then, after reviewing the limitations of existing ML pipeline API frameworks, we discussed how to create a multi-step DL pipeline using MLflow's MLproject framework. We showed a step-by-step approach to creating a three-step DL pipeline using MLproject and how to implement a pipeline function to orchestrate the necessary tasks. We also provided a Python implementation template to help you implement each pipeline task. When running a pipeline with MLflow, we can track the entire pipeline's progress with a parent run_id, and then use a child run_id for each...