Description
| Product ID: | 9781492053194 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | US |
| Title: | Building Machine Learning Pipelines |
| Authors: | Author: Hannes Hapke |
| Page Count: | 364 |
| Subjects: | Privacy and data protection, Privacy & data protection, Network security, Parallel processing, Natural language and machine translation, Machine learning, Neural networks and fuzzy systems, Image processing, Audio processing, Network security, Parallel processing, Natural language & machine translation, Machine learning, Neural networks & fuzzy systems, Image processing, Audio processing |
| Description: | Select Guide Rating 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 pipelineBuild your pipeline using components from TensorFlow ExtendedOrchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow PipelinesWork with data using TensorFlow Data Validation and TensorFlow TransformAnalyze a model in detail using TensorFlow Model AnalysisExamine fairness and bias in your model performanceDeploy models with TensorFlow Serving or TensorFlow Lite for mobile devicesLearn privacy-preserving machine learning techniques |
| Imprint Name: | O'Reilly Media |
| Publisher Name: | O'Reilly Media |
| Country of Publication: | GB |
| Publishing Date: | 2020-08-18 |
