Exploring The Cloud Computing Stack Geeksforgeeks
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Practical Full Stack Machine Learning
Master the ML process, from pipeline development to model deployment in production. KEY FEATURES ● Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API. ● A step-by-step approach to cover every data science task with utmost efficiency and highest performance. ● Access to advanced data engineering and ML tools like AirFlow, MLflow, and ensemble techniques. DESCRIPTION 'Practical Full-Stack Machine Learning' introduces data professionals to a set of powerful, open-source tools and concepts required to build a complete data science project. This book is written in Python, and the ML solutions are language-neutral and can be applied to various software languages and concepts. The book covers data pre-processing, feature management, selecting the best algorithm, model performance optimization, exposing ML models as API endpoints, and scaling ML API. It helps you learn how to use cookiecutter to create reusable project structures and templates. It explains DVC so that you can implement it and reap the same benefits in ML projects.It also covers DASK and how to use it to create scalable solutions for pre-processing data tasks. KerasTuner, an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search will be covered in this book. It explains ensemble techniques such as bagging, stacking, and boosting methods and the ML-ensemble framework to easily and effectively implement ensemble learning. The book also covers how to use Airflow to automate your ETL tasks for data preparation. It explores MLflow, which allows you to train, reuse, and deploy models created with any library. It teaches how to use fastAPI to expose and scale ML models as API endpoints. WHAT YOU WILL LEARN ● Learn how to create reusable machine learning pipelines that are ready for production. ● Implement scalable solutions for pre-processing data tasks using DASK. ● Experiment with ensembling techniques like Bagging, Stacking, and Boosting methods. ● Learn how to use Airflow to automate your ETL tasks for data preparation. ● Learn MLflow for training, reprocessing, and deployment of models created with any library. ● Workaround cookiecutter, KerasTuner, DVC, fastAPI, and a lot more. WHO THIS BOOK IS FOR This book is geared toward data scientists who want to become more proficient in the entire process of developing ML applications from start to finish. Knowing the fundamentals of machine learning and Keras programming would be an essential requirement. TABLE OF CONTENTS 1. Organizing Your Data Science Project 2. Preparing Your Data Structure 3. Building Your ML Architecture 4. Bye-Bye Scheduler, Welcome Airflow 5. Organizing Your Data Science Project Structure 6. Feature Store for ML 7. Serving ML as API
Artificial Intelligence and Large Language Models
Having been catapulted into public discourse in the last few years, this book serves as an in-depth exploration of the ever-evolving domain of artificial intelligence (AI), large language models, and ChatGPT. It provides a meticulous and thorough analysis of AI, ChatGPT technology, and their prospective trajectories given the current trend, in addition to tracing the significant advancements that have materialized over time. Key Features: Discusses the fundamentals of AI for general readers Introduces readers to the ChatGPT chatbot and how it works Covers natural language processing (NLP), the foundational building block of ChatGPT Introduces readers to the deep learning transformer architecture Covers the fundamentals of ChatGPT training for practitioners Illustrated and organized in an accessible manner, this textbook contains particular appeal to students and course convenors at the undergraduate and graduate level, as well as a reference source for general readers.
Cloud Computing
Author: Nikos Antonopoulos
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-07-16
Cloud computing continues to emerge as a subject of substantial industrial and academic interest. Although the meaning and scope of “cloud computing” continues to be debated, the current notion of clouds blurs the distinctions between grid services, web services, and data centers, among other areas. Clouds also bring considerations of lowering the cost for relatively bursty applications to the fore. Cloud Computing: Principles, Systems and Applications is an essential reference/guide that provides thorough and timely examination of the services, interfaces and types of applications that can be executed on cloud-based systems. The book identifies and highlights state-of-the-art techniques and methods for designing cloud systems, presents mechanisms and schemes for linking clouds to economic activities, and offers balanced coverage of all related technologies that collectively contribute towards the realization of cloud computing. With an emphasis on the conceptual and systemic links between cloud computing and other distributed computing approaches, this text also addresses the practical importance of efficiency, scalability, robustness and security as the four cornerstones of quality of service. Topics and features: explores the relationship of cloud computing to other distributed computing paradigms, namely peer-to-peer, grids, high performance computing and web services; presents the principles, techniques, protocols and algorithms that can be adapted from other distributed computing paradigms to the development of successful clouds; includes a Foreword by Professor Mark Baker of the University of Reading, UK; examines current cloud-practical applications and highlights early deployment experiences; elaborates the economic schemes needed for clouds to become viable business models. This book will serve as a comprehensive reference for researchers and students engaged in cloud computing. Professional system architects, technical managers, and IT consultants will also find this unique text a practical guide to the application and delivery of commercial cloud services. Prof. Nick Antonopoulos is Head of the School of Computing, University of Derby, UK. Dr. Lee Gillam is a Lecturer in the Department of Computing at the University of Surrey, UK.