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Practical Deep Learning at Scale with MLflow

You're reading from   Practical Deep Learning at Scale with MLflow Bridge the gap between offline experimentation and online production

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781803241333
Length 288 pages
Edition 1st Edition
Tools
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Author (1):
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Yong Liu Yong Liu
Author Profile Icon Yong Liu
Yong Liu
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1 - Deep Learning Challenges and MLflow Prime
2. Chapter 1: Deep Learning Life Cycle and MLOps Challenges FREE CHAPTER 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.

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