Designing Machine Learning Systems (An Iterative Process for Production-Ready Applications)

By Chip Huyen

Designing Machine Learning Systems (An Iterative Process for Production-Ready Applications)

By Chip Huyen

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Generative Deep Learning (Teaching Machines To Paint, Write, Compose, and Play)

By Foster, David

Generative Deep Learning (Teaching Machines To Paint, Write, Compose, and Play)

By Foster, David

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Introduction to Machine Learning with Python (A Guide for Data Scientists)

By Andreas C. Mueller, Sarah Guido

Introduction to Machine Learning with Python (A Guide for Data Scientists)

By Andreas C. Mueller, Sarah Guido

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577

Reliable Machine Learning (Applying SRE Principles to ML in Production)

By Cathy Chen, Niall Richard Murphy, Kranti Parisa, D Sculley

Reliable Machine Learning (Applying SRE Principles to ML in Production)

By Cathy Chen, Niall Richard Murphy, Kranti Parisa, D Sculley

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Kubeflow for Machine Learning (From Lab to Production)

By Grant, Trevor, Karau, Holden, Boris Lublinsky, Richard Liu

Kubeflow for Machine Learning (From Lab to Production)

By Grant, Trevor, Karau, Holden, Boris Lublinsky, Richard Liu

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Automating Data Quality Monitoring at Scale (Scaling Beyond Rules with Machine Learning)

By Stanley, Jeremy, Schwartz, Paige

Automating Data Quality Monitoring at Scale (Scaling Beyond Rules with Machine Learning)

By Stanley, Jeremy, Schwartz, Paige

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Machine Learning Design Patterns (Solutions to Common Challenges in Data Preparation, Model Building, and MLOps)

By Lakshmanan, Valliappa, Robinson, Sara

Machine Learning Design Patterns (Solutions to Common Challenges in Data Preparation, Model Building, and MLOps)

By Lakshmanan, Valliappa, Robinson, Sara

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Practical Machine Learning for Computer Vision (End-to-End Machine Learning for Images)

By Lakshmanan, Valliappa, Görner, Martin, Ryan Gillard

Practical Machine Learning for Computer Vision (End-to-End Machine Learning for Images)

By Lakshmanan, Valliappa, Görner, Martin, Ryan Gillard

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Practical Mlops (Operationalizing Machine Learning Models)

By Gift, Noah, Deza, Alfredo

Practical Mlops (Operationalizing Machine Learning Models)

By Gift, Noah, Deza, Alfredo

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Machine Learning Interviews (Kickstart Your Machine Learning and Data Career)

By Chang, Susan Shu

Machine Learning Interviews (Kickstart Your Machine Learning and Data Career)

By Chang, Susan Shu

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Learning Tensorflow.Js (Powerful Machine Learning in JavaScript)

By Laborde, Gant

Learning Tensorflow.Js (Powerful Machine Learning in JavaScript)

By Laborde, Gant

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Ai-Powered Business Intelligence (Improving Forecasts and Decision Making with Machine Learning)

By Zwingmann, Tobias

Ai-Powered Business Intelligence (Improving Forecasts and Decision Making with Machine Learning)

By Zwingmann, Tobias

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Introducing MLOps (How to Scale Machine Learning in the Enterprise)

By Treveil, Mark, Omont, Nicolas, Clement Stenac, Kenji Lefevre

Introducing MLOps (How to Scale Machine Learning in the Enterprise)

By Treveil, Mark, Omont, Nicolas, Clement Stenac, Kenji Lefevre

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Architecting Data and Machine Learning Platforms (Enable Analytics and Ai-Driven Innovation in the Cloud)

By Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner

Architecting Data and Machine Learning Platforms (Enable Analytics and Ai-Driven Innovation in the Cloud)

By Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner

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Thoughtful Machine Learning (A Test-Driven Approach)

By Kirk, Matthew

Thoughtful Machine Learning (A Test-Driven Approach)

By Kirk, Matthew

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31

Practical Automated Machine Learning on Azure (Using Azure Machine Learning to Quickly Build AI Solutions)

By Mukunthu, Deepak, Shah, Parashar, Wee Hyong Tok

Practical Automated Machine Learning on Azure (Using Azure Machine Learning to Quickly Build AI Solutions)

By Mukunthu, Deepak, Shah, Parashar, Wee Hyong Tok

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Programming Machine Learning (From Coding to Deep Learning)

By Perrotta, Paolo

Programming Machine Learning (From Coding to Deep Learning)

By Perrotta, Paolo

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Machine Learning and Security (Protecting Systems with Data and Algorithms)

By Chio, Clarence, Freeman, David

Machine Learning and Security (Protecting Systems with Data and Algorithms)

By Chio, Clarence, Freeman, David

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29

Practical Time Series Analysis (Prediction with Statistics and Machine Learning)

By Nielsen, Aileen

Practical Time Series Analysis (Prediction with Statistics and Machine Learning)

By Nielsen, Aileen

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Machine Learning for High-Risk Applications (Techniques for Responsible AI)

By Hall, Patrick, Curtis, James

Machine Learning for High-Risk Applications (Techniques for Responsible AI)

By Hall, Patrick, Curtis, James

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Scaling Machine Learning with Spark (Distributed ML with MLlib, TensorFlow, and PyTorch)

By Polak, Adi

Scaling Machine Learning with Spark (Distributed ML with MLlib, TensorFlow, and PyTorch)

By Polak, Adi

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Practicing Trustworthy Machine Learning (Consistent, Transparent, and Fair AI Pipelines)

By Pruksachatkun, Yada, Mcateer, Matthew, Subhabrata Majumdar

Practicing Trustworthy Machine Learning (Consistent, Transparent, and Fair AI Pipelines)

By Pruksachatkun, Yada, Mcateer, Matthew, Subhabrata Majumdar

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Machine Learning for Email (Spam Filtering and Priority Inbox)

By Conway, Drew, White, John Myles

Machine Learning for Email (Spam Filtering and Priority Inbox)

By Conway, Drew, White, John Myles

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Practical Machine Learning – A New Look at Anomaly Detection

By Ted Dunning, Friedman, Ellen, M.D.

Practical Machine Learning – A New Look at Anomaly Detection

By Ted Dunning, Friedman, Ellen, M.D.

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