Introducing Mlops
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Introducing Mlops
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Author : Mark Treveil
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-11-30
Introducing Mlops written by Mark Treveil 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 2020-11-30 with Computers categories.
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
Introducing Mlops
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Author : Mark Treveil
language : en
Publisher: O'Reilly Media
Release Date : 2020-11-30
Introducing Mlops written by Mark Treveil 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 2020-11-30 with Computers categories.
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
Machine Learning Design Patterns
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Author : Valliappa Lakshmanan
language : en
Publisher: O'Reilly Media
Release Date : 2020-10-15
Machine Learning Design Patterns written by Valliappa Lakshmanan 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 2020-10-15 with Computers categories.
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
Practical Mlops
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Author : Noah Gift
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-09-14
Practical Mlops written by Noah Gift 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 2021-09-14 with Computers categories.
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
Pragmatic Ai
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Author : Noah Gift
language : en
Publisher: Addison-Wesley Professional
Release Date : 2018-07-12
Pragmatic Ai written by Noah Gift and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-12 with Computers categories.
Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Beginning Mlops With Mlflow
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Author : Sridhar Alla
language : en
Publisher:
Release Date : 2021
Beginning Mlops With Mlflow written by Sridhar Alla and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Cloud computing categories.
Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training. The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with varying levels of automation to help you better understand MLOps. MLFlow is covered and you will explore how to integrate MLOps into your existing code, allowing you to easily track metrics, parameters, graphs, and models. You will be guided through the process of deploying and querying your models with AWS SageMaker, Google Cloud, and Microsoft Azure. And you will learn how to integrate your MLOps setups using Databricks. You will: Perform basic data analysis and construct models in scikit-learn and PySpark Train, test, and validate your models (hyperparameter tuning) Know what MLOps is and what an ideal MLOps setup looks like Easily integrate MLFlow into your existing or future projects Deploy your models and perform predictions with them on the cloud.
Machine Learning Engineering With Mlflow
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Author : Natu Lauchande
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-08-27
Machine Learning Engineering With Mlflow written by Natu Lauchande and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-27 with Computers categories.
Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key FeaturesExplore machine learning workflows for stating ML problems in a concise and clear manner using MLflowUse MLflow to iteratively develop a ML model and manage it Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environmentBook Description MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments. This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you'll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins. By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments. What you will learnDevelop your machine learning project locally with MLflow's different featuresSet up a centralized MLflow tracking server to manage multiple MLflow experimentsCreate a model life cycle with MLflow by creating custom modelsUse feature streams to log model results with MLflowDevelop the complete training pipeline infrastructure using MLflow featuresSet up an inference-based API pipeline and batch pipeline in MLflowScale large volumes of data by integrating MLflow with high-performance big data librariesWho this book is for This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.
Managing Ai In The Enterprise
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Author : Klaus Haller
language : en
Publisher:
Release Date : 2022
Managing Ai In The Enterprise written by Klaus Haller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Delivering AI projects and building an AI organization are two big challenges for enterprises. They determine whether companies succeed or fail in establishing AI and integrating AI into their digital transformation. This book addresses both challenges by bringing together organizational and service design concepts, project management, and testing and quality assurance. It covers crucial, often-overlooked topics such as MLOps, IT risk, security and compliance, and AI ethics. In particular, the book shows how to shape AI projects and the capabilities of an AI line organization in an enterprise. It elaborates critical deliverables and milestones, helping you turn your vision into a corporate reality by efficiently managing and setting goals for data scientists, data engineers, and other IT specialists. For those new to AI or AI in an enterprise setting you will find this book a systematic introduction to the field. You will get the necessary know-how to collaborate with and lead AI specialists and guide them to success. Time-pressured readers will benefit from self-contained sections explaining key topics and providing illustrations for fostering discussions in their next team, project, or management meeting. Reading this book helps you to better sell the business benefits from your AI initiatives and build your skills around scoping and delivering AI projects. You will be better able to work through critical aspects such as quality assurance, security, and ethics when building AI solutions in your organization. What You Will Learn Clarify the benefits of your AI initiatives and sell them to senior managers Scope and manage AI projects in your organization Set up quality assurance and testing for AI models and their integration in complex software solutions Shape and manage an AI delivery organization, thereby mastering ML Ops Understand and formulate requirements for the underlying data management infrastructure Handle AI-related IT security, compliance, and risk topics and understand relevant AI ethics aspects .
Introducing Mlops
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Author : Clement Stenac
language : en
Publisher: O'Reilly Media
Release Date : 2021-02-28
Introducing Mlops written by Clement Stenac 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 2021-02-28 with categories.
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
Introducing Mlops
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Author : Mark Treveil
language : en
Publisher:
Release Date : 2021
Introducing Mlops written by Mark Treveil and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Electronic books categories.
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.