Publisher |
Shroff/O'Reilly |
Publication Year |
2020 |
ISBN-13 |
9789385889004 |
ISBN-10 |
9789385889004 |
Binding |
Paperback |
Number of Pages |
368 Pages |
Language |
(English) |
Dimensions (Cms) |
20X14X4 |
Weight (grms) |
600 |
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.
Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.
Understand the steps to build a machine learning pipeline
Build your pipeline using components from TensorFlow Extended
Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines
Work with data using TensorFlow Data Validation and TensorFlow Transform
Analyze a model in detail using TensorFlow Model Analysis
Examine fairness and bias in your model performance
Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices
Learn privacy-preserving machine learning techniques
Hannes Hapke
Hannes is a Senior Machine Learning Engineer at SAP Concur where he focuses on ML Infrastructure and Natural Language Processing projects. Hannes is a Google Developer Expert for Machine Learning and a co-author of machine learning publications like NLP in Action
Hannes Hapke
Shroff/O'Reilly