Hands On Mlops On Azure

DOWNLOAD

Download Hands On Mlops On Azure PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Hands On Mlops On Azure book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Hands On Mlops On Azure

DOWNLOAD
Author : Banibrata De
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-08-01

Hands On Mlops On Azure written by Banibrata De 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 2025-08-01 with Computers categories.

A practical guide to building, deploying, automating, monitoring, and scaling ML and LLM solutions in production Key Features Build reproducible ML pipelines with Azure ML CLI and GitHub Actions Automate ML workflows end to end, including deployment and monitoring Apply LLMOps principles to deploy and manage generative AI responsibly across clouds Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionEffective machine learning (ML) now demands not just building models but deploying and managing them at scale. Written by a seasoned senior software engineer with high-level expertise in both MLOps and LLMOps, Hands-On MLOps on Azure equips ML practitioners, DevOps engineers, and cloud professionals with the skills to automate, monitor, and scale ML systems across environments. The book begins with MLOps fundamentals and their roots in DevOps, exploring training workflows, model versioning, and reproducibility using pipelines. You'll implement CI/CD with GitHub Actions and the Azure ML CLI, automate deployments, and manage governance and alerting for enterprise use. The author draws on their production ML experience to provide you with actionable guidance and real-world examples. A dedicated section on LLMOps covers operationalizing large language models (LLMs) such as GPT-4 using RAG patterns, evaluation techniques, and responsible AI practices. You'll also work with case studies across Azure, AWS, and GCP that offer practical context for multi-cloud operations. Whether you're building pipelines, packaging models, or deploying LLMs, this guide delivers end-to-end strategy to build robust, scalable systems. By the end of this book, you'll be ready to design, deploy, and maintain enterprise-grade ML solutions with confidence. What you will learn Understand the DevOps to MLOps transition Build reproducible, reusable pipelines using the Azure ML CLI Set up CI/CD for training and deployment workflows Monitor ML applications and detect model/data drift Capture and secure governance and lineage data Operationalize LLMs using RAG and prompt flows Apply MLOps across Azure, AWS, and GCP use cases Who this book is for This book is for DevOps and Cloud engineers and SREs interested in or responsible for managing the lifecycle of machine learning models. Professionals who are already familiar with their ML workloads and want to improve their practices, or those who are new to MLOps and want to learn how to effectively manage machine learning models in this environment, will find this book beneficial. The book is also useful for technical decision-makers and project managers looking to understand the process and benefits of MLOps.

Hands On Cloud Analytics With Microsoft Azure Stack

DOWNLOAD
Author : Prashila Naik
language : en
Publisher: BPB Publications
Release Date : 2020-11-12

Hands On Cloud Analytics With Microsoft Azure Stack written by Prashila Naik and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-12 with Computers categories.

Explore and work with various Microsoft Azure services for real-time Data Analytics KEY FEATURESÊ Understanding what Azure can do with your data Understanding the analytics services offered by Azure Understand how data can be transformed to generate more data Understand what is done after a Machine Learning model is builtÊ Go through some Data Analytics real-world use cases ÊÊ DESCRIPTIONÊ Data is the key input for Analytics. Building and implementing data platforms such as Data Lakes, modern Data Marts, and Analytics at scale require the right cloud platform that Azure provides through its services. The book starts by sharing how analytics has evolved and continues to evolve. Following the introduction, you will deep dive into ingestion technologies. You will learn about Data processing services in Azure. You will next learn about what is meant by a Data Lake and understand how Azure Data Lake Storage is used for analytical workloads. You will then learn about critical services that will provide actual Machine Learning capabilities in Azure. The book also talks about Azure Data Catalog for cataloging, Azure AD for Access Management, Web Apps and PowerApps for cloud web applications, Cognitive services for Speech, Vision, Search and Language, Azure VM for computing and Data Science VMs, Functions as serverless computing, Kubernetes and Containers as deployment options. Towards the end, the book discusses two use cases on Analytics. WHAT WILL YOU LEARNÊÊ Explore and work with various Azure services Orchestrate and ingest data using Azure Data Factory Learn how to use Azure Stream Analytics Get to know more about Synapse Analytics and its features Learn how to use Azure Analysis Services and its functionalities Ê WHO THIS BOOK IS FORÊ This book is for anyone who has basic to intermediate knowledge of cloud and analytics concepts and wants to use Microsoft Azure for Data Analytics. This book will also benefit Data Scientists who want to use Azure for Machine Learning. Ê TABLE OF CONTENTSÊÊ 1. Ê Data and its power 2. Ê Evolution of Analytics and its Types 3. Ê Internet of Things 4. Ê AI and ML 5. Ê Why cloud 6. Ê What are a data lake and a modern datamart 7. Ê Introduction to Azure services 8. Ê Types of data 9. Ê Azure Data Factory 10. Stream Analytics 11. Azure Data Lake Store and Azure Storage 12. Cosmos DB 13.Ê Synapse Analytics 14.Ê Azure Databricks 15.Ê Azure Analysis Services 16.Ê Power BI 17.Ê Azure Machine Learning 18.Ê Sample Architectures and synergies - Real-Time and Batch 19.Ê Azure Data Catalog 20.Ê Azure Active Directory 21.Ê Azure Webapps 22.Ê Power apps 23.Ê Time Series Insights 24.Ê Azure Cognitive Services 25.Ê Azure Logicapps 26.Ê Azure VM 27.Ê Azure Functions 28.Ê Azure Containers 29.Ê Azure KubernetesÊ Service 30.Ê Use Case 1 31.Ê Use Case 2

Practical Mlops

DOWNLOAD
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

Engineering Mlops

DOWNLOAD
Author : Emmanuel Raj
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-04-19

Engineering Mlops written by Emmanuel Raj 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 2021-04-19 with Computers categories.

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.

Hands On Edge Analytics With Azure Iot

DOWNLOAD
Author : Colin Dow
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-05-21

Hands On Edge Analytics With Azure Iot written by Colin Dow 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 2020-05-21 with Computers categories.

Design, secure, and protect the privacy of edge analytics applications using platforms and tools such as Microsoft's Azure IoT Edge, MicroPython, and Open Source Computer Vision (OpenCV) Key FeaturesBecome well-versed with best practices for implementing automated analytical computationsDiscover real-world examples to extend cloud intelligenceDevelop your skills by understanding edge analytics and applying it to research activitiesBook Description Edge analytics has gained attention as the IoT model for connected devices rises in popularity. This guide will give you insights into edge analytics as a data analysis model, and help you understand why it’s gaining momentum. You'll begin with the key concepts and components used in an edge analytics app. Moving ahead, you'll delve into communication protocols to understand how sensors send their data to computers or microcontrollers. Next, the book will demonstrate how to design modern edge analytics apps that take advantage of the processing power of modern single-board computers and microcontrollers. Later, you'll explore Microsoft Azure IoT Edge, MicroPython, and the OpenCV visual recognition library. As you progress, you'll cover techniques for processing AI functionalities from the server side to the sensory side of IoT. You'll even get hands-on with designing a smart doorbell system using the technologies you’ve learned. To remove vulnerabilities in the overall edge analytics architecture, you'll discover ways to overcome security and privacy challenges. Finally, you'll use tools to audit and perform real-time monitoring of incoming data and generate alerts for the infrastructure. By the end of this book, you'll have learned how to use edge analytics programming techniques and be able to implement automated analytical computations. What you will learnDiscover the key concepts and architectures used with edge analyticsUnderstand how to use long-distance communication protocols for edge analyticsDeploy Microsoft Azure IoT Edge to a Raspberry PiCreate Node-RED dashboards with MQTT and Text to Speech (TTS)Use MicroPython for developing edge analytics appsExplore various machine learning techniques and discover how machine learning is related to edge analyticsUse camera and vision recognition algorithms on the sensory side to design an edge analytics appMonitor and audit edge analytics appsWho this book is for If you are a data analyst, data architect, or data scientist who is interested in learning and practicing advanced automated analytical computations, then this book is for you. You will also find this book useful if you’re looking to learn edge analytics from scratch. Basic knowledge of data analytics concepts is assumed to get the most out of this book.

Azure Machine Learning Engineering

DOWNLOAD
Author : Sina Fakhraee
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-01-20

Azure Machine Learning Engineering written by Sina Fakhraee 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 2023-01-20 with Computers categories.

Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service Key FeaturesAutomate complete machine learning solutions using Microsoft AzureUnderstand how to productionize machine learning modelsGet to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learningBook Description Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You'll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide. Throughout the book, you'll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You'll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework. By the end of this Azure Machine Learning book, you'll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios. What you will learnTrain ML models in the Azure Machine Learning serviceBuild end-to-end ML pipelinesHost ML models on real-time scoring endpointsMitigate bias in ML modelsGet the hang of using an MLOps framework to productionize modelsSimplify ML model explainability using the Azure Machine Learning service and Azure InterpretWho this book is for Machine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered.

Architecting Intelligent Cloud Systems Ai Mlops And Scalable Infrastructure For The Future

DOWNLOAD
Author : PHANISH LAKKARASU
language : en
Publisher: Global Pen Press UK PUBLICATION
Release Date :

Architecting Intelligent Cloud Systems Ai Mlops And Scalable Infrastructure For The Future written by PHANISH LAKKARASU and has been published by Global Pen Press UK PUBLICATION this book supported file pdf, txt, epub, kindle and other format this book has been release on with Architecture categories.

Hands On Industrial Internet Of Things

DOWNLOAD
Author : Giacomo Veneri
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-11-15

Hands On Industrial Internet Of Things written by Giacomo Veneri 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 2024-11-15 with Technology & Engineering categories.

Build scalable, secure, and intelligent systems by utilizing IoT architectures, AWS, Azure, AI, and real-world solutions to become a skilled IIoT architect Key Features Leverage IoT, AI/ML, and cloud technologies to unlock industrial potential and drive business innovation Work with labs on real-world edge computing scenarios, integrating AWS, Azure, and open source tools Use diagnostic and predictive analytics to develop digital twins, improve industrial processes, and manage assets Purchase of the print or Kindle book includes a free PDF eBook Book Description In today's automation-driven era, precision is crucial, and the Industrial Internet of Things (IIoT) has made a remarkable impact. This updated second edition explores the technologies fueling the IIoT revolution and shares essential knowledge to enable you to establish remote-access networks. Written by IIoT and AI experts, as well as renowned authors, this book helps you enhance your skills in emerging technologies by introducing new techniques from Azure and AWS and keeping you up to date with the latest advancements. You'll find out how Artificial Intelligence of Things (AIoT) and MLOps apply to IIoT and learn how to handle complex projects confidently. The book covers identifying and connecting industrial data sources from various sensors, advancing from foundational concepts to professional skills. You'll discover how to connect these sensors to cloud networks such as AWS IoT, Azure IoT, and open source IoT platforms, and extract data from the cloud to your devices. Through hands-on experience with tools such as Node-RED, OPC UA, MQTT, NoSQL, defense in depth, and Python, you'll develop streaming and batch-based AI algorithms. By the end of this book, you'll have achieved a professional level of expertise in the cloud, IoT, and AI, and be able to build more robust, efficient, and reliable IoT infrastructure for your industry. What will you learn Get a solid understanding of industrial processes, devices, and protocols Harness IoT technology to effectively manage industrial use cases Design and implement an IIoT network flow to continuously monitor the performance of your critical assets Get to grips with popular cloud-based platforms such as AWS and Azure Explore Edge devices and learn about Edge and fog computing to gather field data Apply diagnostic analytics to real-world data to answer critical workforce questions Develop AIoT technology for predictive maintenance Who this book is for If you are an IoT architect, developer, AI engineer, or stakeholder involved in designing the architecture systems of the Industrial Internet of Things, this book is for you. The only prerequisite needed is a solid understanding of the Python programming language and networking concepts.

Azure Data Scientist Associate Certification Guide

DOWNLOAD
Author : Andreas Botsikas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-12-03

Azure Data Scientist Associate Certification Guide written by Andreas Botsikas 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 2021-12-03 with Computers categories.

Develop the skills you need to run machine learning workloads in Azure and pass the DP-100 exam with ease Key FeaturesCreate end-to-end machine learning training pipelines, with or without codeTrack experiment progress using the cloud-based MLflow-compatible process of Azure ML servicesOperationalize your machine learning models by creating batch and real-time endpointsBook Description The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam. What you will learnCreate a working environment for data science workloads on AzureRun data experiments using Azure Machine Learning servicesCreate training and inference pipelines using the designer or codeDiscover the best model for your dataset using Automated MLUse hyperparameter tuning to optimize trained modelsDeploy, use, and monitor models in productionInterpret the predictions of a trained modelWho this book is for This book is for developers who want to infuse their applications with AI capabilities and data scientists looking to scale their machine learning experiments in the Azure cloud. Basic knowledge of Python is needed to follow the code samples used in the book. Some experience in training machine learning models in Python using common frameworks like scikit-learn will help you understand the content more easily.

Data Science Solutions On Azure

DOWNLOAD
Author : Julian Soh
language : en
Publisher: Springer Nature
Release Date : 2024-11-18

Data Science Solutions On Azure written by Julian Soh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-18 with Computers categories.

This revamped and updated book focuses on the latest in AI technology—Generative AI. It builds on the first edition by moving away from traditional data science into the area of applied AI using the latest breakthroughs in Generative AI. Based on real-world projects, this edition takes a deep look into new concepts and approaches such as Prompt Engineering, testing and grounding of Large Language Models, fine tuning, and implementing new solution architectures such as Retrieval Augmented Generation (RAG). You will learn about new embedded AI technologies in Search, such as Semantic and Vector Search. Written with a view on how to implement Generative AI in software, this book contains examples and sample code. In addition to traditional Data Science experimentation in Azure Machine Learning (AML) that was covered in the first edition, the authors cover new tools such as Azure AI Studio, specifically for testing and experimentation with Generative AI models. What's New in this Book Provides new concepts, tools, and technologies such as Large and Small Language Models, Semantic Kernel, and Automatic Function Calling Takes a deeper dive into using Azure AI Studio for RAG and Prompt Engineering design Includes new and updated case studies for Azure OpenAI Teaches about Copilots, plugins, and agents What You'll Learn Get up to date on the important technical aspects of Large Language Models, based on Azure OpenAI as the reference platform Know about the different types of models: GPT3.5 Turbo, GPT4, GPT4o, Codex, DALL-E, and Small Language Models such as Phi-3 Develop new skills such as Prompt Engineering and fine tuning of Large/Small Language Models Understand and implement new architectures such as RAG and Automatic Function Calling Understand approaches for implementing Generative AI using LangChain and Semantic Kernel See how real-world projects help you identify great candidates for Applied AI projects, including Large/Small Language Models Who This Book Is For Software engineers and architects looking to deploy end-to-end Generative AI solutions on Azure with the latest tools and techniques.