Mastering Ai And Machine Learning For Devops Engineers

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Mastering Ai And Machine Learning For Devops Engineers

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Author : Shantanu Dalal
language : en
Publisher: Independently Published
Release Date : 2025-01-29

Mastering Ai And Machine Learning For Devops Engineers written by Shantanu Dalal and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-29 with Computers categories.

Mastering AI and Machine Learning for DevOps Engineers - Unlock the Power of AI in DevOps The world of DevOps is evolving rapidly, and Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential tools for automating, optimizing, and scaling modern development and deployment pipelines. Mastering AI and Machine Learning for DevOps Engineers is a comprehensive guide designed to help professionals integrate AI-driven techniques into DevOps workflows, enhancing efficiency, reliability, and innovation. This book bridges the gap between DevOps practices and AI capabilities, providing practical insights, real-world examples, and hands-on techniques that empower engineers, developers, and IT professionals to leverage ML models for automation, predictive analytics, and intelligent decision-making. What You'll Learn in This Book: ✅ Fundamentals of AI & ML for DevOps - Understand key AI/ML concepts and how they apply to automation, monitoring, and infrastructure optimization. ✅ Automating DevOps Pipelines with AI - Learn how AI enhances CI/CD workflows, error detection, and self-healing systems. ✅ Predictive Analytics in DevOps - Use ML models to forecast system failures, optimize resources, and improve software delivery cycles. ✅ AI-Driven Monitoring & Observability - Implement AI-powered tools for log analysis, anomaly detection, and security threat mitigation. ✅ Infrastructure as Code (IaC) with AI - Enhance Terraform, Kubernetes, and cloud automation with intelligent decision-making models. ✅ Machine Learning in Cloud & Edge Computing - Deploy AI models in AWS, Azure, and Kubernetes clusters for efficient DevOps operations. ✅ ChatOps & AI Assistants for DevOps - Integrate AI-driven bots and virtual assistants for faster collaboration and incident response. ✅ Real-World Use Cases & Case Studies - Explore successful AI implementations in DevOps and how leading tech companies leverage ML-driven automation. Who Is This Book For? DevOps Engineers & SREs looking to integrate AI into their automation pipelines. Software Developers & Architects who want to understand how ML enhances modern application development. IT Managers & Cloud Engineers aiming to implement AI-powered monitoring, security, and infrastructure automation. Data Scientists & AI Enthusiasts exploring AI-driven solutions for DevOps challenges. Why This Book?

Mastering Mlops Architecture From Code To Deployment

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Author : Raman Jhajj
language : en
Publisher: BPB Publications
Release Date : 2023-12-12

Mastering Mlops Architecture From Code To Deployment written by Raman Jhajj and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-12 with Computers categories.

Harness the power of MLOps for managing real time machine learning project cycle KEY FEATURES ● Comprehensive coverage of MLOps concepts, architecture, tools and techniques. ● Practical focus on building end-to-end ML Systems for Continual Learning with MLOps. ● Actionable insights on CI/CD, monitoring, continual model training and automated retraining. DESCRIPTION MLOps, a combination of DevOps, data engineering, and machine learning, is crucial for delivering high-quality machine learning results due to the dynamic nature of machine learning data. This book delves into MLOps, covering its core concepts, components, and architecture, demonstrating how MLOps fosters robust and continuously improving machine learning systems. By covering the end-to-end machine learning pipeline from data to deployment, the book helps readers implement MLOps workflows. It discusses techniques like feature engineering, model development, A/B testing, and canary deployments. The book equips readers with knowledge of MLOps tools and infrastructure for tasks like model tracking, model governance, metadata management, and pipeline orchestration. Monitoring and maintenance processes to detect model degradation are covered in depth. Readers can gain skills to build efficient CI/CD pipelines, deploy models faster, and make their ML systems more reliable, robust and production-ready. Overall, the book is an indispensable guide to MLOps and its applications for delivering business value through continuous machine learning and AI. WHAT YOU WILL LEARN ● Architect robust MLOps infrastructure with components like feature stores. ● Leverage MLOps tools like model registries, metadata stores, pipelines. ● Build CI/CD workflows to deploy models faster and continually. ● Monitor and maintain models in production to detect degradation. ● Create automated workflows for retraining and updating models in production. WHO THIS BOOK IS FOR Machine learning specialists, data scientists, DevOps professionals, software development teams, and all those who want to adopt the DevOps approach in their agile machine learning experiments and applications. Prior knowledge of machine learning and Python programming is desired. TABLE OF CONTENTS 1. Getting Started with MLOps 2. MLOps Architecture and Components 3. MLOps Infrastructure and Tools 4. What are Machine Learning Systems? 5. Data Preparation and Model Development 6. Model Deployment and Serving 7. Continuous Delivery of Machine Learning Models 8. Continual Learning 9. Continuous Monitoring, Logging, and Maintenance

Ai Devops And Security Engineering For Futureready Insurance And Financial Services

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Author : Balaji Adusupalli
language : en
Publisher: AQUA PUBLICATIONS
Release Date :

Ai Devops And Security Engineering For Futureready Insurance And Financial Services written by Balaji Adusupalli and has been published by AQUA PUBLICATIONS this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

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Mastering Design Patterns For Layered Testing Master Strategic Test Design Enhance Automation And Integrate Ci Cd Seamlessly Across Api And Ui Layers With Python

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Author : Manish Saini
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-04-19

Mastering Design Patterns For Layered Testing Master Strategic Test Design Enhance Automation And Integrate Ci Cd Seamlessly Across Api And Ui Layers With Python written by Manish Saini 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 2025-04-19 with Computers categories.

Master Layered Test Automation and Build Integrate and Deploy with Confidence Key Features● Implement automated testing across UI, API, and backend for robust coverage● Seamlessly integrate test automation with CI/CD pipelines for efficiency● Master advanced testing strategies for microservices and distributed systems Book DescriptionIn today’s fast-paced software development landscape, ensuring application quality requires a strategic approach to test automation. Mastering Design Patterns for Layered Testing empowers you with the knowledge and tools to design, implement, and optimize automation across UI, API, and backend layers using Python’s powerful ecosystem. Starting with foundational concepts like test design patterns and the Test Pyramid, the book delves into practical implementations of unit testing, integration testing, API testing, and contract testing. You’ll learn how to integrate automated tests into CI/CD pipelines using GitHub Actions, generate detailed test reports with Allure, and address modern testing challenges such as microservices and containerized environments. Real-world case studies illustrate how to apply these techniques in production settings. A dedicated chapter on Generative AI in testing explores its applications in test case generation and test data creation. Whether you're an intermediate tester looking to enhance your automation skills or an experienced professional seeking to learn advanced strategies, this book provides the expertise needed to build scalable and reliable test automation frameworks that drive software quality and efficiency. Stay ahead of the curve—master next-gen test automation before it’s too late! What you will learn● Design and implement scalable test automation across all application layers● Build robust test frameworks using Python’s advanced testing ecosystem● Seamlessly integrate automated tests into modern CI/CD pipelines● Apply advanced testing patterns for APIs, microservices, and UI components● Utilize contract testing and performance testing for reliable applications● Leverage Generative AI to enhance test coverage and efficiency

Mastering Devops On Microsoft Power Platform

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Author : Uroš Kastelic
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-09-05

Mastering Devops On Microsoft Power Platform written by Uroš Kastelic 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 2024-09-05 with Computers categories.

Learn from Microsoft Power Platform experts how to leverage GitHub, Azure DevOps, and GenAI tools like Microsoft Copilots to develop and deliver secure, enterprise-scale solutions Key Features Customize Power Platform for secure large-scale deployments with the help of DevSecOps practices Implement code-first fusion projects with ALM and infuse AI in Power Platform using copilots and ChatOps Get hands-on experience through real-world examples using Azure DevOps and GitHub Purchase of the print or Kindle book includes a free PDF eBook Book Description Mastering DevOps on Microsoft Power Platform is your guide to revolutionizing business-critical solution development. Written by two Microsoft Technology Specialists with extensive experience in enterprise-scale Power Platform implementations and DevOps practices, this book teaches you how to design, build, and secure efficient DevOps processes by adapting custom software development practices to the Power Platform toolset, dramatically reducing time, cost, and errors in app modernization and quality assurance. The book introduces application life cycle management (ALM) and DevOps-enabled architecture, design patterns, and CI/CD practices, showing you why companies adopt DevOps with Power Platform. You'll master environment and solution management using Dataverse, Git, the Power Platform CLI, Azure DevOps, and GitHub Copilot. Implementing the shift-left approach in DevSecOps using GitHub Advanced Security features, you’ll create a Power Platform tenant governed by controls, automated tests, and backlog management. You’ll also discover advanced concepts, such as fusion architecture, pro-dev extensibility, and AI-infused applications, along with tips to avoid common pitfalls. By the end of this book, you’ll be able to build CI/CD pipelines from development to production, enhancing the life cycle of your business solutions on Power Platform. What you will learn Gain insights into ALM and DevOps on Microsoft Power Platform Set up Power Platform pipelines and environments by leveraging best practices Automate, test, monitor, and secure CI/CD pipelines using DevSecOps tools, such as VS Code and GitHub Advanced Security, on Power Platform Enable pro-developer extensibility using fusion development to integrate Azure and Power Platform Provision enterprise landing zones and build well-architected workloads Discover GenAI capabilities in Power Platform and support ChatOps with the copilot stack Who this book is for If you are a DevOps engineer, cloud architect, site reliability engineer, solutions architect, software developer, or low-code engineer looking to master end-to-end DevSecOps implementation on Microsoft Power Platform from basic to advanced levels, this book is for you. Prior knowledge of software development processes and tools is necessary. A basic understanding of Power Platform and DevOps processes will also be beneficial.

Mastering Cloud Native Microservices

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Author : Chetan Walia
language : en
Publisher: BPB Publications
Release Date : 2023-06-14

Mastering Cloud Native Microservices written by Chetan Walia and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Computers categories.

Get familiar with the principles and techniques for designing cost-effective and scalable cloud-native apps with microservices KEY FEATURES ● Gain a comprehensive understanding of the key concepts and strategies involved in building successful cloud-native microservices applications. ● Discover the practical techniques and methodologies for implementing cloud-native microservices. ● Get insights and best practices for implementing cloud-native microservices. DESCRIPTION Microservices-based cloud-native applications are software applications that combine the architectural principles of microservices with the advantages of cloud-native infrastructure and services. If you want to build scalable, resilient, and agile software solutions that can adapt to the dynamic needs of the modern digital landscape, then this book is for you. This comprehensive guide explores the world of cloud-native microservices and their impact on modern application design. The book covers fundamental principles, adoption frameworks, design patterns, and communication strategies specific to microservices. It then emphasizes on the benefits of scalability, fault tolerance, and resource utilization. Furthermore, the book also addresses event-driven data management, serverless approaches, and security by design. All in all, this book is an essential resource that will help you to leverage the power of microservices in your cloud-native applications. By the end of the book, you will gain valuable insights into building scalable, resilient, and future-proof applications in the era of digital transformation. WHAT YOU WILL LEARN ● Gain insight into the fundamental principles and frameworks that form the foundation of modern application design. ● Explore a comprehensive collection of design patterns tailored specifically for microservices architecture. ● Discover a variety of strategies and patterns to effectively facilitate communication between microservices, ensuring efficient collaboration within the system. ● Learn about event-driven data management techniques that enable real-time processing and efficient handling of data in a distributed microservices environment. ● Understand the significance of security-by-design principles and acquire strategies for ensuring the security of microservices architectures. WHO THIS BOOK IS FOR This book is suitable for cloud architects, developers, and practitioners who are interested in learning about design patterns and strategies for building, testing, and deploying cloud-native microservices. It is also valuable for techno-functional roles, solution experts, pre-sales professionals, and anyone else seeking practical knowledge of cloud-native microservices. TABLE OF CONTENTS 1. Cloud-Native Microservices 2. Modern Application Design Principles 3. Microservice Adoption Framework 4. Design Patterns for Microservices 5. Cloud-Powered Microservices 6. Monolith to Microservices Case Study 7. Inter-Service Communication 8. Event-Driven Data Management 9. The Serverless Approach 10. Cloud Microservices - Security by Design 11. Cloud Migration Strategy

Mastering Azure Machine Learning

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Author : Christoph Körner
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-30

Mastering Azure Machine Learning written by Christoph Körner 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 2020-04-30 with Computers categories.

Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes Key FeaturesMake sense of data on the cloud by implementing advanced analyticsTrain and optimize advanced deep learning models efficiently on Spark using Azure DatabricksDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)Book Description The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure. What you will learnSetup your Azure Machine Learning workspace for data experimentation and visualizationPerform ETL, data preparation, and feature extraction using Azure best practicesImplement advanced feature extraction using NLP and word embeddingsTrain gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine LearningUse hyperparameter tuning and Azure Automated Machine Learning to optimize your ML modelsEmploy distributed ML on GPU clusters using Horovod in Azure Machine LearningDeploy, operate and manage your ML models at scaleAutomated your end-to-end ML process as CI/CD pipelines for MLOpsWho this book is for This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.

Mastering Databricks Lakehouse Platform

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Author : Sagar Lad
language : en
Publisher: BPB Publications
Release Date : 2022-07-11

Mastering Databricks Lakehouse Platform written by Sagar Lad and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-11 with Computers categories.

Enable data and AI workloads with absolute security and scalability KEY FEATURES ● Detailed, step-by-step instructions for every data professional starting a career with data engineering. ● Access to DevOps, Machine Learning, and Analytics wirthin a single unified platform. ● Includes design considerations and security best practices for efficient utilization of Databricks platform. DESCRIPTION Starting with the fundamentals of the databricks lakehouse platform, the book teaches readers on administering various data operations, including Machine Learning, DevOps, Data Warehousing, and BI on the single platform. The subsequent chapters discuss working around data pipelines utilizing the databricks lakehouse platform with data processing and audit quality framework. The book teaches to leverage the Databricks Lakehouse platform to develop delta live tables, streamline ETL/ELT operations, and administer data sharing and orchestration. The book explores how to schedule and manage jobs through the Databricks notebook UI and the Jobs API. The book discusses how to implement DevOps methods on the Databricks Lakehouse platform for data and AI workloads. The book helps readers prepare and process data and standardizes the entire ML lifecycle, right from experimentation to production. The book doesn't just stop here; instead, it teaches how to directly query data lake with your favourite BI tools like Power BI, Tableau, or Qlik. Some of the best industry practices on building data engineering solutions are also demonstrated towards the end of the book. WHAT YOU WILL LEARN ● Acquire capabilities to administer end-to-end Databricks Lakehouse Platform. ● Utilize Flow to deploy and monitor machine learning solutions. ● Gain practical experience with SQL Analytics and connect Tableau, Power BI, and Qlik. ● Configure clusters and automate CI/CD deployment. ● Learn how to use Airflow, Data Factory, Delta Live Tables, Databricks notebook UI, and the Jobs API. WHO THIS BOOK IS FOR This book is for every data professional, including data engineers, ETL developers, DB administrators, Data Scientists, SQL Developers, and BI specialists. You don't need any prior expertise with this platform because the book covers all the basics. TABLE OF CONTENTS 1. Getting started with Databricks Platform 2. Management of Databricks Platform 3. Spark, Databricks, and Building a Data Quality Framework 4. Data Sharing and Orchestration with Databricks 5. Simplified ETL with Delta Live Tables 6. SCD Type 2 Implementation with Delta Lake 7. Machine Learning Model Management with Databricks 8. Continuous Integration and Delivery with Databricks 9. Visualization with Databricks 10. Best Security and Compliance Practices of Databricks

Mastering Enterprise Platform Engineering

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Author : Mark Peters
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-06-27

Mastering Enterprise Platform Engineering written by Mark Peters 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 2025-06-27 with Computers categories.

Unlock the full potential of enterprise platforms and drive the future of your business by incorporating cutting-edge gen AI techniques Key Features Apply proven frameworks and real-world strategies to design scalable, high-performing platforms Integrate AI-powered observability, security, compliance into your platform using best practices Work through hands-on tutorials and case studies to implement platform engineering successfully for measurable business impact Purchase of the print or Kindle book includes a free PDF eBook Book Description Modern organizations must deliver software faster, ensure platform stability, and adopt AI, all while reducing operational complexity and cost. But fragmented tooling, scaling challenges, and limited developer enablement hinder progress – driving engineering leaders to seek a cohesive strategy for efficiency, resilience, and innovation. In this book, Dr. Mark Peters and Dr. Gautham Pallapa join forces to resolve these complexities by showing you how to build scalable platforms, operate them efficiently through automation and AI, and optimize software delivery pipelines for continuous value. The chapters cover core principles, including platform architecture, self-service enablement, and developer experience. You’ll explore proven frameworks for cultural transformation, strategic alignment, and continuous improvement, along with 10 bold predictions about the future of platform engineering to help you anticipate trends and lead through change with confidence. By the end of this book, you’ll be able to design and implement resilient, intelligent platforms, accelerate innovation, and drive measurable business impact, positioning you and your organization as leaders in the next era of platform engineering. What you will learn Discover how modern platform engineering drives scalability and sustainable business value Design and implement internal developer platforms with self-service, golden paths, and AI automation Integrate AI and machine learning for predictive observability and smart workload optimization Use leadership and cultural transformation frameworks to build high-performance platform teams Measure and optimize platform success through KPIs and FinOps strategies Accelerate software delivery by unifying existing tools and workflows into cohesive, scalable platforms Who this book is for This book is for experienced professionals across IT, product, and business functions who are responsible for building, operating, optimizing, or scaling platform capabilities. It is tailored for platform engineers, DevOps engineers, software developers, IT operations teams, transformation leaders, and business executives looking to align platform strategy with organizational goals. A solid understanding of DevOps practices, cloud-native technologies, and software development lifecycles, as well as familiarity with CI/CD, infrastructure automation, and modern application deployment is a must.

Supercomputing

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Author : Vladimir Voevodin
language : en
Publisher: Springer Nature
Release Date : 2022-12-15

Supercomputing written by Vladimir Voevodin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-15 with Computers categories.

This book constitutes the refereed proceedings of the 8th Russian Supercomputing Days on Supercomputing, RuSCDays 2022, which took place in Moscow, Russia, in September 2022. The 49 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: Supercomputer Simulation; HPC, BigData, AI: Architectures, Technologies, Tools; Distributed and Cloud Computing.