Building Machine Learning Pipelines
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Building Machine Learning Pipelines
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Author : Hannes Hapke
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-07-13
Building Machine Learning Pipelines written by Hannes Hapke and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-13 with Computers categories.
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
Building Machine Learning Pipelines
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Author : Hannes Hapke
language : en
Publisher: O'Reilly Media
Release Date : 2020-09-08
Building Machine Learning Pipelines written by Hannes Hapke and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-08 with Computers categories.
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. The book also explores new approaches for integrating data privacy into machine learning pipelines. Understand the machine learning management lifecycle Implement data pipelines with Apache Airflow and Kubeflow Pipelines Work with data using TensorFlow tools like ML Metadata, TensorFlow Data Validation, and TensorFlow Transform Analyze models with TensorFlow Model Analysis and ship them with the TFX Model Pusher Component after the ModelValidator TFX Component confirmed that the analysis results are an improvement Deploy models in a variety of environments with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js Learn methods for adding privacy, including differential privacy with TensorFlow Privacy and federated learning with TensorFlow Federated Design model feedback loops to increase your data sets and learn when to update your machine learning models
Deep Learning Pipeline
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Author : Hisham El-Amir
language : en
Publisher: Apress
Release Date : 2019-12-20
Deep Learning Pipeline written by Hisham El-Amir and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-20 with Computers categories.
Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets. You'll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, Deep Learning Pipeline is for you! What You'll Learn Develop a deep learning project using data Study and apply various models to your data Debug and troubleshoot the proper model suited for your data Who This Book Is For Developers, analysts, and data scientists looking to add to or enhance their existing skills by accessing some of the most powerful recent trends in data science. Prior experience in Python or other TensorFlow related languages and mathematics would be helpful.
Machine Learning Production Systems
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Author : Robert Crowe
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-10-02
Machine Learning Production Systems written by Robert Crowe and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Computers categories.
Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines
Hands On Machine Learning With C
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Author : Kirill Kolodiazhnyi
language : en
Publisher:
Release Date : 2020-05-15
Hands On Machine Learning With C written by Kirill Kolodiazhnyi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-15 with Computers categories.
This book will help you explore how to implement different well-known machine learning algorithms with various C++ frameworks and libraries. You will cover basic to advanced machine learning concepts with practical and easy to follow examples. By the end of the book, you will be able to build various machine learning models with ease.
Building Scalable Deep Learning Pipelines On Aws
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Author : Abdelaziz Testas
language : en
Publisher: Apress
Release Date : 2024-12-03
Building Scalable Deep Learning Pipelines On Aws written by Abdelaziz Testas and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.
This book is your comprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologies--such as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3--to streamline the development, training, and deployment of deep learning models. Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale. By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today's data-driven landscape. What You Will Learn Maximize AWS services for scalable and high-performance deep learning architectures Harness the capacity of PyTorch and TensorFlow for advanced neural network development Utilize PySpark for efficient distributed data processing on AWS Orchestrate complex workflows with Apache Airflow for seamless data processing, model training, and deployment Who This Book Is For Data scientists looking to expand their skill set to include deep learning on AWS, machine learning engineers tasked with designing and deploying machine learning systems who want to incorporate deep learning capabilities into their applications, AI practitioners working across various industries who seek to leverage deep learning for solving complex problems and gaining a competitive advantage
Machine Learning Production Systems
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Author : Robert Crowe
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-10-02
Machine Learning Production Systems written by Robert Crowe and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Computers categories.
Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines
Ultimate Mlops For Machine Learning Models Use Real Case Studies To Efficiently Build Deploy And Scale Machine Learning Pipelines With Mlops
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Author : Saurabh D.
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-08-30
Ultimate Mlops For Machine Learning Models Use Real Case Studies To Efficiently Build Deploy And Scale Machine Learning Pipelines With Mlops written by Saurabh D. and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
The only MLOps guide you'll ever need Key Features● Acquire a comprehensive understanding of the entire MLOps lifecycle, from model development to monitoring and governance. ● Gain expertise in building efficient MLOps pipelines with the help of practical guidance with real-world examples and case studies. ● Develop advanced skills to implement scalable solutions by understanding the latest trends/tools and best practices. Book DescriptionThis book is an essential resource for professionals aiming to streamline and optimize their machine learning operations. This comprehensive guide provides a thorough understanding of the MLOps life cycle, from model development and training to deployment and monitoring. By delving into the intricacies of each phase, the book equips readers with the knowledge and tools needed to create robust, scalable, and efficient machine learning workflows. Key chapters include a deep dive into essential MLOps tools and technologies, effective data pipeline management, and advanced model optimization techniques. The book also addresses critical aspects such as scalability challenges, data and model governance, and security in machine learning operations. Each topic is presented with practical insights and real-world case studies, enabling readers to apply best practices in their job roles. Whether you are a data scientist, ML engineer, or IT professional, this book empowers you to take your machine learning projects from concept to production with confidence. It equips you with the practical skills to ensure your models are reliable, secure, and compliant with regulations. By the end, you will be well-positioned to navigate the ever-evolving landscape of MLOps and unlock the true potential of your machine learning initiatives. What you will learn ● Implement and manage end-to-end machine learning lifecycles. ● Utilize essential tools and technologies for MLOps effectively. ● Design and optimize data pipelines for efficient model training. ● Develop and train machine learning models with best practices. ● Deploy, monitor, and maintain models in production environments. ● Address scalability challenges and solutions in MLOps. ● Implement robust security practices to protect your ML systems. ● Ensure data governance, model compliance, and security in ML operations. ● Understand emerging trends in MLOps and stay ahead of the curve. Table of Contents1. Introduction to MLOps 2. Understanding Machine Learning Lifecycle 3. Essential Tools and Technologies in MLOps 4. Data Pipelines and Management in MLOps 5. Model Development and Training 6. Model Optimization Techniques for Performance 7. Efficient Model Deployment and Monitoring Strategies 8. Scalability Challenges and Solutions in MLOps 9. Data, Model Governance, and Compliance in Production Environments 10. Security in Machine Learning Operations 11. Case Studies and Future Trends in MLOps Index
Data Foundations For Ai Systems
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Author : Leon Amsel
language : en
Publisher: Independently Published
Release Date : 2025-10-28
Data Foundations For Ai Systems written by Leon Amsel and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-28 with Computers categories.
Data Foundations for AI Systems: Build Reliable Machine Learning Pipelines that Power Accurate, Scalable, and Trustworthy Models Why do so many AI initiatives fail, not because the models are wrong, but because the data behind them can't be trusted? Every data professional has faced it: a model that performs perfectly in testing but unravels in production. The culprit isn't magic; it's weak data foundations. Without structured, governed, and observable data pipelines, even the smartest algorithms crumble under drift, latency, and inconsistency. Data Foundations for AI Systems is the definitive practical guide to building machine learning pipelines that work reliably, every time. It translates the complex, often chaotic reality of AI data operations into clear, actionable engineering principles grounded in production experience. Through real-world patterns, reproducible frameworks, and field-tested strategies, this book shows how to architect systems where data quality, versioning, observability, and scalability are built in, not bolted on. It bridges the gap between data engineering, data science, and MLOps, helping you create infrastructure that empowers, not obstructs, your models. You'll learn how to: Design scalable data pipelines that serve both training and inference workloads. Build feature stores that ensure consistent, reusable model inputs. Enforce data contracts, lineage, and quality gates across every stage of the pipeline. Implement versioning, reproducibility, and rollback strategies that make audits effortless. Monitor data and model drift in production before performance collapses. Align data engineering and machine learning teams through shared metrics and SLAs. Each chapter walks you through a vital layer of a modern AI data stack, from ingestion to serving, complete with real-world case studies and design templates you can adapt immediately. If you're a data engineer, machine learning practitioner, or technical leader tired of firefighting broken pipelines and inconsistent results, this book delivers the frameworks and practices you need to build dependable, production-grade AI systems. Build your competitive edge on reliable data, not reactive fixes. Your AI models are only as strong as the pipelines beneath them, make them unbreakable.
Building Machine Learning Systems With A Feature Store
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Author : Jim Dowling
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-11-06
Building Machine Learning Systems With A Feature Store written by Jim Dowling and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-06 with Computers categories.
Get up to speed on a new unified approach to building machine learning (ML) systems with a feature store. Using this practical book, data scientists and ML engineers will learn in detail how to develop and operate batch, real-time, and agentic ML systems. Author Jim Dowling introduces fundamental principles and practices for developing, testing, and operating ML and AI systems at scale. You'll see how any AI system can be decomposed into independent feature, training, and inference pipelines connected by a shared data layer. Through example ML systems, you'll tackle the hardest part of ML systems--the data, learning how to transform data into features and embeddings, and how to design a data model for AI. Develop batch ML systems at any scale Develop real-time ML systems by shifting left or shifting right feature computation Develop agentic ML systems that use LLMs, tools, and retrieval-augmented generation Understand and apply MLOps principles when developing and operating ML systems