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 opening chapter, we implemented our first DL model by following the pretrain plus fine-tuning core DL development paradigm using PyTorch lightning-flash for a text sentiment classification model. We learned about the five stages of the full life cycle of DL. We defined the concept of MLOps along with the three foundation layers and four ML/DL pillars, where MLflow plays critical roles in all four pillars (data, model, code, and explainability). Finally, we described the challenges in DL data, model, code, and explainability.

With the knowledge and first DL model experience gained in this chapter, we are now ready to learn about and implement MLflow in our DL model in the following chapters. In the next chapter, we will start with the implementation of a DL model with MLflow autologging enabled.