Download or read book Operationalizing Machine Learning Pipelines written by Vishwajyoti Pandey and published by BPB Publications. This book was released on 2022-02-22 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implementing ML pipelines using MLOps KEY FEATURES ● In-depth knowledge of MLOps, including recommendations for tools and processes. ● Includes only open-source cloud-agnostic tools for demonstrating MLOps. ● Covers end-to-end examples of implementing the whole process on Google Cloud Platform. DESCRIPTION This book will provide you with an in-depth understanding of MLOps and how you can use it inside an enterprise. Each tool discussed in this book has been thoroughly examined, providing examples of how to install and use them, as well as sample data. This book will teach you about every stage of the machine learning lifecycle and how to implement them within an organisation using a machine learning framework. With GitOps, you'll learn how to automate operations and create reusable components such as feature stores for use in various contexts. You will learn to create a server-less training and deployment platform that scales automatically based on demand. You will learn about Polyaxon for machine learning model training, and KFServing, for model deployment. Additionally, you will understand how you should monitor machine learning models in production and what factors can degrade the model's performance. You can apply the knowledge gained from this book to adopt MLOps in your organisation and tailor the requirements to your specific project. As you keep an eye on the model's performance, you'll be able to train and deploy it more quickly and with greater confidence. WHAT YOU WILL LEARN ● Quick grasp of the entire machine learning lifecycle and tricks to manage all components. ● Learn to train and validate machine learning models for scalability. ● Get to know the pros of cloud computing for scaling ML operations. ● Covers aspects of ML operations, such as reproducibility and scalability, in detail. ● Get to know how to monitor machine learning models in production. ● Learn and practice automating the ML training and deployment processes. WHO THIS BOOK IS FOR This book is intended for machine learning specialists, data scientists, and data engineers who wish to improve and increase their MLOps knowledge to streamline machine learning initiatives. Readers with a working knowledge of the machine learning lifecycle would be advantageous. TABLE OF CONTENTS 1. DS/ML Projects – Initial Setup 2. ML Projects Lifecycle 3. ML Architecture – Framework and Components 4. Data Exploration and Quantifying Business Problem 5. Training & Testing ML model 6. ML model performance measurement 7. CRUD operations with different JavaScript frameworks 8. Feature Store 9. Building ML Pipeline

Download or read book Practical MLOps written by Noah Gift and published by "O'Reilly Media, Inc.". This book was released on 2021-09-14 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

Download or read book Building Machine Learning Pipelines written by Hannes Hapke and published by O'Reilly Media. This book was released on 2020-07-13 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Implementing MLOps in the Enterprise written by Yaron Haviv and published by "O'Reilly Media, Inc.". This book was released on 2023-11-30 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: Learn the MLOps process, including its technological and business value Build and structure effective MLOps pipelines Efficiently scale MLOps across your organization Explore common MLOps use cases Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI Learn how to prepare for and adapt to the future of MLOps Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy

Download or read book ML Ops written by David Sweenor and published by . This book was released on 2020 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren't truly operational, these models can't possibly do what you've trained them to do. This report introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach-Build, Manage, Deploy and Integrate, and Monitor-for creating ML-infused applications within your organization. You'll learn how to: Fulfill data science value by reducing friction throughout ML pipelines and workflows Constantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracy Design the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainable Operationalize ML models not only for pipeline deployment but also for external business systems that are more complex and less standardized Put the four-step Build, Manage, Deploy and Integrate, and Monitor approach into action.

Download or read book Introducing MLOps written by Mark Treveil and published by "O'Reilly Media, Inc.". This book was released on 2020-11-30 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized

Download or read book MLOps Engineering at Scale written by Carl Osipov and published by Simon and Schuster. This book was released on 2022-03-22 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools! In MLOps Engineering at Scale you will learn: Extracting, transforming, and loading datasets Querying datasets with SQL Understanding automatic differentiation in PyTorch Deploying model training pipelines as a service endpoint Monitoring and managing your pipeline’s life cycle Measuring performance improvements MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities. About the technology A production-ready machine learning system includes efficient data pipelines, integrated monitoring, and means to scale up and down based on demand. Using cloud-based services to implement ML infrastructure reduces development time and lowers hosting costs. Serverless MLOps eliminates the need to build and maintain custom infrastructure, so you can concentrate on your data, models, and algorithms. About the book MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production. What's inside Reduce or eliminate ML infrastructure management Learn state-of-the-art MLOps tools like PyTorch Lightning and MLFlow Deploy training pipelines as a service endpoint Monitor and manage your pipeline’s life cycle Measure performance improvements About the reader Readers need to know Python, SQL, and the basics of machine learning. No cloud experience required. About the author Carl Osipov implemented his first neural net in 2000 and has worked on deep learning and machine learning at Google and IBM. Table of Contents PART 1 - MASTERING THE DATA SET 1 Introduction to serverless machine learning 2 Getting started with the data set 3 Exploring and preparing the data set 4 More exploratory data analysis and data preparation PART 2 - PYTORCH FOR SERVERLESS MACHINE LEARNING 5 Introducing PyTorch: Tensor basics 6 Core PyTorch: Autograd, optimizers, and utilities 7 Serverless machine learning at scale 8 Scaling out with distributed training PART 3 - SERVERLESS MACHINE LEARNING PIPELINE 9 Feature selection 10 Adopting PyTorch Lightning 11 Hyperparameter optimization 12 Machine learning pipeline

Download or read book Introducing MLOps written by Mark Treveil and published by . This book was released on 2021 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Instead, many of these ML models do nothing more than provide static insights in a slideshow. If they aren't truly operational, these models can't possibly do what you've trained them to do. This book introduces practical concepts to help data scientists and application engineers operationalize ML models to drive real business change. Through lessons based on numerous projects around the world, six experts in data analytics provide an applied four-step approach--Build, Manage, Deploy and Integrate, and Monitor--for creating ML-infused applications within your organization. You'll learn how to: Fulfill data science value by reducing friction throughout ML pipelines and workflows Constantly refine ML models through retraining, periodic tuning, and even complete remodeling to ensure long-term accuracy Design the ML Ops lifecycle to ensure that people-facing models are unbiased, fair, and explainable Operationalize ML models not only for pipeline deployment but also for external business systems that are more complex and less standardized Put the four-step Build, Manage, Deploy and Integrate, and Monitor approach into action.

Download or read book Ultimate MLOps for Machine Learning Models written by Saurabh Dorle and published by Orange Education Pvt Ltd. This book was released on 2024-08-30 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: TAGLINE 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. DESCRIPTION This 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 WILL YOU 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. WHO IS THIS BOOK FOR? This book is for data scientists, machine learning engineers, and data engineers aiming to master MLOps for effective model management in production. It’s also ideal for researchers and stakeholders seeking insights into how MLOps drives business strategy and scalability, as well as anyone with a basic grasp of Python and machine learning looking to enter the field of data science in production. TABLE OF CONTENTS 1. 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

Download or read book ML Ops written by David Sweenor and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Implementing MLOps in the Enterprise written by Yaron Haviv and published by "O'Reilly Media, Inc.". This book was released on 2023-11-30 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: Learn the MLOps process, including its technological and business value Build and structure effective MLOps pipelines Efficiently scale MLOps across your organization Explore common MLOps use cases Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI Learn how to prepare for and adapt to the future of MLOps Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy

Download or read book Machine Learning Governance for Managers written by Francesca Lazzeri and published by Springer Nature. This book was released on with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Engineering MLOps written by Emmanuel Raj and published by Packt Publishing Ltd. This book was released on 2021-04-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is for This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book.

Download or read book Official Google Cloud Certified Professional Data Engineer Study Guide written by Dan Sullivan and published by John Wiley & Sons. This book was released on 2020-05-11 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proven Study Guide that prepares you for this new Google Cloud exam The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications. Build and operationalize storage systems, pipelines, and compute infrastructure Understand machine learning models and learn how to select pre-built models Monitor and troubleshoot machine learning models Design analytics and machine learning applications that are secure, scalable, and highly available. This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform.

Download or read book Operating AI written by Ulrika Jagare and published by John Wiley & Sons. This book was released on 2022-04-19 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you’ll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world—and not just the lab—Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.

Download or read book Machine Learning Design Patterns written by Valliappa Lakshmanan and published by O'Reilly Media. This book was released on 2020-10-15 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly

Download or read book Building Machine Learning Pipelines written by Hannes Hapke and published by O'Reilly Media. This book was released on 2020-09-08 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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