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

Chapter 6: Running Hyperparameter Tuning at Scale

Hyperparameter tuning or hyperparameter optimization (HPO) is a procedure that finds the best possible deep neural network structures, types of pretrained models, and model training process within a reasonable computing resource constraint and time frame. Here, hyperparameter refers to parameters that cannot be changed or learned during the ML training process, such as the number of layers inside a deep neural network, the choice of a pretrained language model, or the learning rate, batch size, and optimizer of the training process. In this chapter, we will use HPO as a shorthand to refer to the process of hyperparameter tuning and optimization. HPO is a critical step for producing a high-performance ML/DL model. Given that the search space of the hyperparameter is very large, efficiently running HPO at scale is a major challenge. The complexity and high cost of evaluating a DL model, compared to classical ML models, further compound...