You're reading from Practical Deep Learning at Scale with MLflow

Product type Book

Published in Jul 2022

Publisher Packt

ISBN-13 9781803241333

Pages 288 pages

Edition 1st Edition

Languages

Concepts

Deep Learning

Author (1):

Yong Liu

Table of Contents (17) Chapters

Preface

1. Section 1 - Deep Learning Challenges and MLflow Prime

2. Chapter 1: Deep Learning Life Cycle and MLOps Challenges

3. Chapter 2: Getting Started with MLflow for Deep Learning

4. Section 2 – Tracking a Deep Learning Pipeline at Scale

5. Chapter 3: Tracking Models, Parameters, and Metrics

6. Chapter 4: Tracking Code and Data Versioning

7. Section 3 – Running Deep Learning Pipelines at Scale

8. Chapter 5: Running DL Pipelines in Different Environments

9. Chapter 6: Running Hyperparameter Tuning at Scale

10. Section 4 – Deploying a Deep Learning Pipeline at Scale

11. Chapter 7: Multi-Step Deep Learning Inference Pipeline

12. Chapter 8: Deploying a DL Inference Pipeline at Scale

13. Section 5 – Deep Learning Model Explainability at Scale

14. Chapter 9: Fundamentals of Deep Learning Explainability

15. Chapter 10: Implementing DL Explainability with MLflow

16. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "For learning purposes, we have provided two example mlruns artifacts and the huggingface cache folder in the GitHub repository under the chapter08 folder."

A block of code is set as follows:

client = boto3.client('sagemaker-runtime')

response = client.invoke_endpoint(

        EndpointName=app_name,

        ContentType=content_type,

        Accept=accept,

        Body=payload

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

loaded_model = mlflow.pyfunc.spark_udf(

    spark,

    model_uri=logged_model,

    result_type=StringType())

Any command-line input or output is written as follows:

mlflow models serve -m models:/inference_pipeline_model/6

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "To execute the code in this cell, you can just click on Run Cell in the top-right drop-down menu."

Tips or Important Notes

Appear like this.