Ebook: Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow
Author: Hannes Hapke Catherine Nelson
- Genre: Computers // Cybernetics: Artificial Intelligence
- Tags: Google Cloud Platform, Machine Learning, TensorFlow, Pipelines, Deployment, Directed Acyclic Graphs, TensorFlow Lite, Pipeline Orchestration, TensorFlow Extended, Apache Beam, Data Ingestion, Data Preparation, Data Validation, Data Preprocessing, Model Training, Model Analysis, Model Explainability, Model Deployment, TensorFlow Serving, Apache Airflow, Kubeflow Pipelines, Feedback Loops, Data Privacy, Differential Privacy, TensorFlow Privacy
- Year: 2020
- Publisher: O'Reilly Media
- City: Sebastopol, CA
- Edition: 1
- Language: English
- pdf
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 that make up 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 convert them to TensorFlow Lite for mobile devices
• Understand privacy-preserving machine learning techniques
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 that make up 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 convert them to TensorFlow Lite for mobile devices
• Understand privacy-preserving machine learning techniques
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