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Machine Learning Engineering  with Python

You're reading from   Machine Learning Engineering with Python Manage the lifecycle of machine learning models using MLOps with practical examples

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781837631964
Length 462 pages
Edition 2nd Edition
Languages
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Author (1):
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Andrew P. McMahon Andrew P. McMahon
Author Profile Icon Andrew P. McMahon
Andrew P. McMahon
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Toc

Table of Contents (12) Chapters Close

Preface 1. Introduction to ML Engineering 2. The Machine Learning Development Process FREE CHAPTER 3. From Model to Model Factory 4. Packaging Up 5. Deployment Patterns and Tools 6. Scaling Up 7. Deep Learning, Generative AI, and LLMOps 8. Building an Example ML Microservice 9. Building an Extract, Transform, Machine Learning Use Case 10. Other Books You May Enjoy
11. Index

Index

Symbols

*args 147

**kwargs 147, 148

A

abstraction 157

activation function 322

adaptive learning rate methods

AdaDelta 88

AdaGrad 88

Adam 88

RMSprop 88

advanced Airflow features

ETML pipeline, building with 405-417

Agile

versus Waterfall 51

Agile Manifesto

URL 49

Airflow DAG 75

Airflow role 242

reference link 242

Amazon CLI, configuring

reference link 215

Amazon SageMaker 246

Amazon Web Services (AWS) 39

Apache Airflow 232-235

CI/CD pipelines, setting up 244, 245

on AWS 235-243

used, for building pipelines 232

using, pros and cons 403

Apache Kafka 209

Apache Spark 206

Apache Storm 209

APIs

large language models (LLMs), consuming via 342-345

application

containerizing, with Docker 384, 385

app.py 378

Artifact store 247

artificial general intelligence (AGI) 341

artificial intelligence (AI) 319

Artificial Neural Networks...

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