Ultimate Mlops For Machine Learning Models

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Ultimate Mlops For Machine Learning Models

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Author : Saurabh Dorle
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-08-30

Ultimate Mlops For Machine Learning Models written by Saurabh Dorle and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.

TAGLINE The only MLOps guide you'll ever need KEY FEATURES ● Acquire a comprehensive understanding of the entire MLOps lifecycle, from model development to monitoring and governance. ● Gain expertise in building efficient MLOps pipelines with the help of practical guidance with real-world examples and case studies. ● Develop advanced skills to implement scalable solutions by understanding the latest trends/tools and best practices. DESCRIPTION This book is an essential resource for professionals aiming to streamline and optimize their machine learning operations. This comprehensive guide provides a thorough understanding of the MLOps life cycle, from model development and training to deployment and monitoring. By delving into the intricacies of each phase, the book equips readers with the knowledge and tools needed to create robust, scalable, and efficient machine learning workflows. Key chapters include a deep dive into essential MLOps tools and technologies, effective data pipeline management, and advanced model optimization techniques. The book also addresses critical aspects such as scalability challenges, data and model governance, and security in machine learning operations. Each topic is presented with practical insights and real-world case studies, enabling readers to apply best practices in their job roles. Whether you are a data scientist, ML engineer, or IT professional, this book empowers you to take your machine learning projects from concept to production with confidence. It equips you with the practical skills to ensure your models are reliable, secure, and compliant with regulations. By the end, you will be well-positioned to navigate the ever-evolving landscape of MLOps and unlock the true potential of your machine learning initiatives. WHAT WILL YOU LEARN ● Implement and manage end-to-end machine learning lifecycles. ● Utilize essential tools and technologies for MLOps effectively. ● Design and optimize data pipelines for efficient model training. ● Develop and train machine learning models with best practices. ● Deploy, monitor, and maintain models in production environments. ● Address scalability challenges and solutions in MLOps. ● Implement robust security practices to protect your ML systems. ● Ensure data governance, model compliance, and security in ML operations. ● Understand emerging trends in MLOps and stay ahead of the curve. WHO IS THIS BOOK FOR? This book is for data scientists, machine learning engineers, and data engineers aiming to master MLOps for effective model management in production. It’s also ideal for researchers and stakeholders seeking insights into how MLOps drives business strategy and scalability, as well as anyone with a basic grasp of Python and machine learning looking to enter the field of data science in production. TABLE OF CONTENTS 1. Introduction to MLOps 2. Understanding Machine Learning Lifecycle 3. Essential Tools and Technologies in MLOps 4. Data Pipelines and Management in MLOps 5. Model Development and Training 6. Model Optimization Techniques for Performance 7. Efficient Model Deployment and Monitoring Strategies 8. Scalability Challenges and Solutions in MLOps 9. Data, Model Governance, and Compliance in Production Environments 10. Security in Machine Learning Operations 11. Case Studies and Future Trends in MLOps Index

Machine Learning Infrastructure And Best Practices For Software Engineers

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Author : Miroslaw Staron
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31

Machine Learning Infrastructure And Best Practices For Software Engineers written by Miroslaw Staron 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 2024-01-31 with Computers categories.

Efficiently transform your initial designs into big systems by learning the foundations of infrastructure, algorithms, and ethical considerations for modern software products Key Features Learn how to scale-up your machine learning software to a professional level Secure the quality of your machine learning pipeline at runtime Apply your knowledge to natural languages, programming languages, and images Book DescriptionAlthough creating a machine learning pipeline or developing a working prototype of a software system from that pipeline is easy and straightforward nowadays, the journey toward a professional software system is still extensive. This book will help you get to grips with various best practices and recipes that will help software engineers transform prototype pipelines into complete software products. The book begins by introducing the main concepts of professional software systems that leverage machine learning at their core. As you progress, you’ll explore the differences between traditional, non-ML software, and machine learning software. The initial best practices will guide you in determining the type of software you need for your product. Subsequently, you will delve into algorithms, covering their selection, development, and testing before exploring the intricacies of the infrastructure for machine learning systems by defining best practices for identifying the right data source and ensuring its quality. Towards the end, you’ll address the most challenging aspect of large-scale machine learning systems – ethics. By exploring and defining best practices for assessing ethical risks and strategies for mitigation, you will conclude the book where it all began – large-scale machine learning software.What you will learn Identify what the machine learning software best suits your needs Work with scalable machine learning pipelines Scale up pipelines from prototypes to fully fledged software Choose suitable data sources and processing methods for your product Differentiate raw data from complex processing, noting their advantages Track and mitigate important ethical risks in machine learning software Work with testing and validation for machine learning systems Who this book is for If you’re a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product.

Data Engineering Best Practices

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Author : Richard J. Schiller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-10-11

Data Engineering Best Practices written by Richard J. Schiller 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 2024-10-11 with Computers categories.

Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.

Exam Ref Dp 100 Designing And Implementing A Data Science Solution On Azure

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Author : Dayne Sorvisto
language : en
Publisher: Microsoft Press
Release Date : 2024-12-06

Exam Ref Dp 100 Designing And Implementing A Data Science Solution On Azure written by Dayne Sorvisto and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-06 with Computers categories.

Prepare for Microsoft Exam DP-100 and demonstrate your real-world knowledge of managing data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning, and MLflow. Designed for professionals with data science experience, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure Data Scientist Associate level. Focus on the expertise measured by these objectives: Design and prepare a machine learning solution Explore data and train models Prepare a model for deployment Deploy and retrain a model This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you have experience in designing and creating a suitable working environment for data science workloads, training machine learning models, and managing, deploying, and monitoring scalable machine learning solutions About the Exam Exam DP-100 focuses on knowledge needed to design and prepare a machine learning solution, manage an Azure Machine Learning workspace, explore data and train models, create models by using the Azure Machine Learning designer, prepare a model for deployment, manage models in Azure Machine Learning, deploy and retrain a model, and apply machine learning operations (MLOps) practices. About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Azure Data Scientist Associate credential, demonstrating your expertise in applying data science and machine learning to implement and run machine learning workloads on Azure, including knowledge and experience using Azure Machine Learning and MLflow.

Artificial Intelligence For Devops And Site Reliability Engineering Theories Applications And Future Directions

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Author : Swarup Panda
language : en
Publisher: Deep Science Publishing
Release Date : 2025-08-07

Artificial Intelligence For Devops And Site Reliability Engineering Theories Applications And Future Directions written by Swarup Panda and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-07 with Computers categories.

This book offers an in-depth examination of the transformative impact Artificial Intelligence (AI) and Machine Learning (ML) have on DevOps and Site Reliability Engineering (SRE). It sits at the intersection of the cutting edge in AI and at how actual operations can use smart technology to refine your CI/CD pipeline, tell when incidents are rolling your way, help to automate resolution and improve the eyes on monitoring. Readers will learn complete details on AI-driven observability, finding anomalies, performance tuning, and capacity planning—helping organizations to predict failures, improve up times and accelerate software with a rock rock-solid foundation. With clear and detailed explanations, bolstered by case studies with leaders from the industry, and actionable frameworks to implementation, DevOps engineers, SRE professionals, and IT executives will learn how to effectively operationalize AI within their environments. It also includes critical content on AI ethics, transparency, and governance—a must for today's high-stakes production environments. Readers will walk away fully prepared to use AI to automate the repetitive and time-consuming tasks based on data and to make data-informed decisions that strengthen their infrastructure and deliver operational excellence.

Artificial Intelligence Applications And Innovations

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Author : Ilias Maglogiannis
language : en
Publisher: Springer Nature
Release Date : 2025-06-21

Artificial Intelligence Applications And Innovations written by Ilias Maglogiannis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-21 with Computers categories.

This four-volume set constitutes the proceedings of the 21st IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2025, which was held in Limassol, Cyprus, during June 2025. The 123 full papers and 7 short papers were presented in this volume were carefully reviewed and selected from 303 submissions. They focus on ethical-moral AI aspects related to its Environmental impact, Privacy, Transparency, Bias, Discrimination and Fairness.

Proceedings Of Fourth International Conference On Computing And Communication Networks

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Author : Akshi Kumar
language : en
Publisher: Springer Nature
Release Date : 2025-05-24

Proceedings Of Fourth International Conference On Computing And Communication Networks written by Akshi Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-24 with Technology & Engineering categories.

This book includes selected peer-reviewed papers presented at fourth International Conference on Computing and Communication Networks (ICCCN 2024), held at Manchester Metropolitan University, UK, during 17–18 October 2024. The book covers topics of network and computing technologies, artificial intelligence and machine learning, security and privacy, communication systems, cyber physical systems, data analytics, cyber security for industry 4.0, and smart and sustainable environmental systems.

Surfacing Best Practices For Ai Software Development And Integration In Healthcare

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Author : Mark Sendak
language : en
Publisher: Frontiers Media SA
Release Date : 2023-06-08

Surfacing Best Practices For Ai Software Development And Integration In Healthcare written by Mark Sendak and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-08 with Medical categories.

Service Oriented Computing

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Author : Marco Aiello
language : en
Publisher: Springer Nature
Release Date : 2023-10-11

Service Oriented Computing written by Marco Aiello and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-11 with Computers categories.

This book constitutes the refereed proceedings of the 17th Symposium and Summer School, SummerSOC 2023, held in Heraklion, Crete, Greece, in June 25–July 1, 2023. The 6 full papers and 3 short papers presented in this book were carefully reviewed and selected from 27 submissions. They are organized in the following sections as follows: Distributed Systems; Smart; and Mixed Technologies.

Practical Mlops

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Author : Noah Gift
language : en
Publisher:
Release Date : 2021

Practical Mlops written by Noah Gift and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with 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.