Mlops Fundamentals
DOWNLOAD
Download Mlops Fundamentals PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Mlops Fundamentals 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
Mlops Fundamentals
DOWNLOAD
Author : Booker Blunt
language : en
Publisher: Independently Published
Release Date : 2025-07-11
Mlops Fundamentals written by Booker Blunt and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-11 with Computers categories.
Unlock the power of machine learning in production environments. MLOps Fundamentals is your comprehensive guide to deploying, managing, and scaling machine learning models in production. Whether you're an aspiring MLOps engineer, data scientist, or software developer, this book equips you with the foundational knowledge and practical tools to move machine learning models from experimentation to real-world deployment. You'll learn how to build end-to-end machine learning pipelines, automate workflows, and monitor model performance in production. From model training and testing to versioning and deployment, MLOps Fundamentals covers it all-ensuring your models run smoothly, efficiently, and securely. Inside, you'll discover how to: Set up a complete MLOps workflow using tools like Docker, Kubernetes, and CI/CD Automate model training, testing, and deployment with MLFlow and Kubeflow Version and manage models using tools like DVC and ModelDB Build robust pipelines that handle data preprocessing, training, and deployment Monitor and manage deployed models for performance, accuracy, and drift Scale machine learning models with cloud platforms like AWS, Google Cloud, and Azure Implement model rollback, A/B testing, and continuous integration strategies Ensure security and governance in an MLOps environment Collaborate with teams effectively using best practices in MLOps culture With hands-on examples, code snippets, and real-world scenarios, this book helps you apply MLOps principles to make machine learning models production-ready and scalable. Whether you're deploying models for web apps, customer insights, or predictive maintenance, MLOps Fundamentals provides the knowledge and tools to bring your AI models to life in production.
Artificial Intelligence
DOWNLOAD
Author : David R. Martinez
language : en
Publisher: MIT Press
Release Date : 2024-06-11
Artificial Intelligence written by David R. Martinez and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-11 with Computers categories.
The first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities. Most books on artificial intelligence (AI) focus on a single functional building block, such as machine learning or human-machine teaming. Artificial Intelligence takes a more holistic approach, addressing AI from the view of systems engineering. The book centers on the people-process-technology triad that is critical to successful development of AI products and services. Development starts with an AI design, based on the AI system architecture, and culminates with successful deployment of the AI capabilities. Directed toward AI developers and operational users, this accessibly written volume of the MIT Lincoln Laboratory Series can also serve as a text for undergraduate seniors and graduate-level students and as a reference book. Key features: In-depth look at modern computing technologies Systems engineering description and means to successfully undertake an AI product or service development through deployment Existing methods for applying machine learning operations (MLOps) AI system architecture including a description of each of the AI pipeline building blocks Challenges and approaches to attend to responsible AI in practice Tools to develop a strategic roadmap and techniques to foster an innovative team environment Multiple use cases that stem from the authors’ MIT classes, as well as from AI practitioners, AI project managers, early-career AI team leaders, technical executives, and entrepreneurs Exercises and Jupyter notebook examples
Mlops Fundamentals
DOWNLOAD
Author : Piper Shaw
language : en
Publisher:
Release Date : 2025-10-10
Mlops Fundamentals written by Piper Shaw and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-10 with Computers categories.
Unlock the power of MLOps to bridge machine learning and DevOps, transforming experimental models into scalable production systems. "MLOps Fundamentals: Master Machine Learning Operations with Hands-On Azure Demos" guides beginners through building CI/CD pipelines using Azure DevOps and Azure ML. Piper Shaw provides step-by-step tutorials, real-world case studies from Netflix and Uber, and practical exercises to tackle data drift, model decay, and deployment bottlenecks. Learn data versioning with DVC, automate workflows, deploy endpoints, monitor performance, and optimize costs. Ideal for data scientists, engineers, and teams deploying ML at scale in finance, retail, and beyond-gain the skills to deliver reliable, efficient models that drive business value.
Ace The Google Machine Learning Engineer Certification
DOWNLOAD
Author : Etienne Noumen
language : en
Publisher: Etienne Noumen
Release Date :
Ace The Google Machine Learning Engineer Certification written by Etienne Noumen and has been published by Etienne Noumen this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Master Google Cloud’s most advanced AI certification with this definitive 2025 study guide. From TensorFlow and data pipelines to ML ops, model deployment, and ethical AI—this book delivers the knowledge, tools, and confidence to help you ace the Professional Machine Learning Engineer Exam. Backed by real-world examples, mock exams, and hands-on insights. 🎯 The ins and outs of Google's Machine Learning Engineer certification are explored in detail. A comprehensive guide is provided, covering the latest updates and offering tips for success. Why This Certification Matters - The growing demand for skilled Machine Learning Engineers - Career advancement and increased earning potential - The Google brand and its weight in the tech world Decoding the Certification: Requirements & Exam Structure - The four main exam domains: Machine Learning Concepts, Data Analysis, Model Building and Evaluation, and Machine Learning Systems Design - Exam format and structure: Multiple-choice, coding, and open-ended questions - The Google Cloud Platform (GCP) proficiency requiredMastering the Material: Essential Skills & Resources - Key concepts: Supervised and unsupervised learning, deep learning, natural language processing, computer vision - Recommended resources: Coursera, Udacity, Google Cloud Skills Boost, and relevant online communities - Practical projects: Building your own portfolio to showcase your skills Strategies for Success: Effective Preparation & Exam Day Tips - Practice, practice, practice: Using mock exams, coding exercises, and real-world datasets - Time management: Balancing learning, practice, and exam-day strategy - Stress management: Techniques to stay calm and focused on exam day Full Practice Exam - 2025 included Beyond the Certification: Career Paths & Continued Learning - The book explores potential roles: Machine Learning Engineer, Data Scientist, AI Researcher - The importance of continuous learning and staying updated with advancements in the field - Building your professional network and actively contributing to the ML community 📘 Download the E-Book + Audiobook combo at Djamgatech at https://djamgatech.com/product/ace-the-google-machine-learning-engineer-certification-2025-update-e-book-audiobook/ 📘 You can also Download the E-Book + Audiobook combo at Google Play Books at https://play.google.com/store/audiobooks/details?id=AQAAAEDKqGjosM
Ace The Aws Certified Ai Practitioner Exam Aws Aif C01
DOWNLOAD
Author : Etienne Noumen
language : en
Publisher: Etienne Noumen
Release Date :
Ace The Aws Certified Ai Practitioner Exam Aws Aif C01 written by Etienne Noumen and has been published by Etienne Noumen this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
📘 Ace the AWS Certified AI Practitioner Exam: Your Comprehensive Guide is your one-stop resource to master AI, ML, and GenAI concepts—without being a data scientist! 🎓 Learn how to PASS the AWS Certified AI Practitioner Exam (AIF-C01) — even if you’re NEW to AI! In this video, we break down the top strategies and insider tips for acing the AWS AI Practitioner cert: ⚡️ Exam domains explained (AI, ML, GenAI, Responsible AI, Security) ⚡️ AWS Bedrock, SageMaker, Comprehend, Clarify, and more ⚡️ Sample questions & answers in the actual exam format ⚡️ Success stories from Reddit + common mistakes to avoid
Computer Vision On Aws
DOWNLOAD
Author : Lauren Mullennex
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-03-31
Computer Vision On Aws written by Lauren Mullennex 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 2023-03-31 with Computers categories.
Develop scalable computer vision solutions for real-world business problems and discover scaling, cost reduction, security, and bias mitigation best practices with AWS AI/ML services Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn how to quickly deploy and automate end-to-end CV pipelines on AWS Implement design principles to mitigate bias and scale production of CV workloads Work with code examples to master CV concepts using AWS AI/ML services Book Description Computer vision (CV) is a field of artificial intelligence that helps transform visual data into actionable insights to solve a wide range of business challenges. This book provides prescriptive guidance to anyone looking to learn how to approach CV problems for quickly building and deploying production-ready models. You'll begin by exploring the applications of CV and the features of Amazon Rekognition and Amazon Lookout for Vision. The book will then walk you through real-world use cases such as identity verification, real-time video analysis, content moderation, and detecting manufacturing defects that'll enable you to understand how to implement AWS AI/ML services. As you make progress, you'll also use Amazon SageMaker for data annotation, training, and deploying CV models. In the concluding chapters, you'll work with practical code examples, and discover best practices and design principles for scaling, reducing cost, improving the security posture, and mitigating bias of CV workloads. By the end of this AWS book, you'll be able to accelerate your business outcomes by building and implementing CV into your production environments with the help of AWS AI/ML services. What you will learn Apply CV across industries, including e-commerce, logistics, and media Build custom image classifiers with Amazon Rekognition Custom Labels Create automated end-to-end CV workflows on AWS Detect product defects on edge devices using Amazon Lookout for Vision Build, deploy, and monitor CV models using Amazon SageMaker Discover best practices for designing and evaluating CV workloads Develop an AI governance strategy across the entire machine learning life cycle Who this book is for If you are a machine learning engineer or data scientist looking to discover best practices and learn how to build comprehensive CV solutions on AWS, this book is for you. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.
Privileged Access For Models
DOWNLOAD
Author : Daniel Mercery
language : en
Publisher: Daniel Mercery
Release Date :
Privileged Access For Models written by Daniel Mercery and has been published by Daniel Mercery this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
AI platforms rely on privileged credentials—API keys, service accounts, and administrative access—that, if mismanaged, can expose entire systems to compromise. Privileged Access for Models is a technical guide for security engineers responsible for securing secrets, keys, and administrative access across machine learning infrastructure. The book focuses on practical controls that reduce blast radius while maintaining operational efficiency. It addresses privileged access as a first-class risk in AI systems, not an afterthought inherited from traditional IT environments. Readers will learn how to: Identify privileged access paths across AI platforms and pipelines Secure model API keys, tokens, and service credentials Apply least-privilege principles to model training and inference Integrate secrets management into MLOps workflows Monitor and audit privileged access to AI infrastructure Reduce credential sprawl across cloud and ML environments This book helps teams prevent high-impact security failures by bringing discipline and visibility to privileged access in AI systems.
Secure Mlops Fundamentals
DOWNLOAD
Author : Daniel Mercery
language : en
Publisher: Daniel Mercery
Release Date :
Secure Mlops Fundamentals written by Daniel Mercery and has been published by Daniel Mercery this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Machine learning systems are now business-critical infrastructure, yet many organizations deploy models without the operational security discipline applied to traditional software. Secure MLOps Fundamentals provides a practical, end-to-end security framework for building, deploying, and operating machine learning systems safely at scale. This book translates security principles into concrete MLOps controls that engineering and platform teams can apply immediately, without academic theory or vendor bias. Inside, readers will learn how to: Secure training data, features, and labels against tampering and leakage Harden model development pipelines and CI/CD workflows Manage secrets, credentials, and access across ML environments Apply model signing, versioning, and artifact integrity controls Detect drift, abuse, and anomalous behavior in production models Integrate security reviews into MLOps without slowing delivery Written for real-world environments, this guide aligns security, DevOps, and ML practices into a single operational model. It is designed as both a reference and an implementation checklist for teams responsible for production AI.
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.
Mlops Lifecycle Toolkit
DOWNLOAD
Author : Dayne Sorvisto
language : en
Publisher: Apress
Release Date : 2023-09-04
Mlops Lifecycle Toolkit written by Dayne Sorvisto and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-04 with Computers categories.
This book is aimed at practitioners of data science, with consideration for bespoke problems, standards, and tech stacks between industries. It will guide you through the fundamentals of technical decision making, including planning, building, optimizing, packaging, and deploying end-to-end, reliable, and robust stochastic workflows using the language of data science. MLOps Lifecycle Toolkit walks you through the principles of software engineering, assuming no prior experience. It addresses the perennial “why” of MLOps early, along with insight into the unique challenges of engineering stochastic systems. Next, you’ll discover resources to learn software craftsmanship, data-driven testing frameworks, and computer science. Additionally, you will see how to transition from Jupyter notebooks to code editors, and leverage infrastructure and cloud services to take control of the entire machine learning lifecycle. You’ll gain insight into the technical and architectural decisions you’re likely to encounter, as well as best practices for deploying accurate, extensible, scalable, and reliable models. Through hands-on labs, you will build your own MLOps “toolkit” that you can use to accelerate your own projects. In later chapters, author Dayne Sorvisto takes a thoughtful, bottom-up approach to machine learning engineering by considering the hard problems unique to industries such as high finance, energy, healthcare, and tech as case studies, along with the ethical and technical constraints that shape decision making. After reading this book, whether you are a data scientist, product manager, or industry decision maker, you will be equipped to deploy models to production, understand the nuances of MLOps in the domain language of your industry, and have the resources for continuous delivery and learning. What You Will Learn Understand the principles of software engineering and MLOps Design an end-to-end machine learning system Balance technical decisions and architectural trade-offs Gain insight into the fundamental problems unique to each industry and how to solve them Who This Book Is For Data scientists, machine learning engineers, and software professionals.