Building Machine Learning Pipelines

Hannes Hapke is a senior data scientist for SAP Concur's Concur Labs, where he investigates novel methods to apply machine learning to improve the business traveler experience. Catherine Nelson is also a Senior Data Scientist for Concur Labs at SAP Concur, where she investigates novel methods to apply machine learning to improve the business traveler experience.

Companies are investing billions of dollars in machine learning initiatives, but the money is a waste if the models cannot be properly applied. Hannes Hapke and Catherine Nelson bring you through the steps of automating a machine learning pipeline using the TensorFlow environment in this practical guide. In Building Machine Learning Pipelines, you'll discover approaches and tools for reducing deployment time from days to minutes, allowing you to focus on designing new models rather than managing legacy systems.

Data scientists, machine learning engineers, and DevOps engineers will learn how to successfully productize their data science initiatives beyond model development, while managers will gain a better understanding of their role in accelerating these projects.

• Learn about the stages that comprise a machine learning pipeline.
• Create your pipeline with TensorFlow Extended components.
• Use Apache Beam, Apache Airflow, and Kubeflow Pipelines to orchestrate your machine learning workflow.
• Use TensorFlow Data Validation and TensorFlow Transform to work with data.
• Using TensorFlow Model Analysis, you can thoroughly examine a model.
• Examine your model's performance for fairness and bias.
• Models can be deployed using TensorFlow Serving or converted to TensorFlow Lite for mobile devices.
• Learn about machine learning approaches that protect your privacy.

Author: Hannes Hapke and Catherine Nelson

Link to buy: https://www.amazon.com/Building-Machine-Learning-Pipelines-Automating/dp/1492053198/

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