Titre: Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow
�diteur: O'Reilly Media
Date de parution: 2020
Langue: anglais
ISBN � 10 chiffres: 1492053198
ISBN � 13 chiffres: 9781492053194
Reliure: paperback
�tat de l'article: New

A propos de ce titre

Synopsis

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

� propos des auteurs

Hannes Hapke is a VP of Engineering at Caravel, a machine learning company providing novel personalization products for the retail industry. Prior to joining Caravel, Hannes was a Ssenior data science engineer at Cambia Health Solutions, a health solutions provider for 2.6 million people and a machine learning engineer at Talentpair, Inc., where he developed novel deep learning model for recruiting companies. Hannes cofounded a renewable energy startup which applied deep learning to detect homes would be optimal candidates for solar power. Additionally, Hannes has coauthored a publication about natural language processing and deep learning and presented at various conferences about deep learning and Python.

Catherine Nelson is a senior data scientist for Concur Labs at SAP Concur, where she explores innovative ways to use machine learning to improve the experience of a business traveller. She is particularly interested in privacy-preserving ML and applying deep learning to enterprise data. In her previous career as a geophysicist she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University.

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