Essentials of Python for Artificial Intelligence and Machine Learning by
This book introduces the essentials of Python for the emerging fields of Machine Learning (ML) and Artificial Intelligence (AI). The authors explore the use of Python's advanced module features and apply them in probability, statistical testing, signal processing, financial forecasting, and various other applications. This includes mathematical operations with array data structures, Data Manipulation, Data Cleaning, machine learning, Data pipeline, probability density functions, interpolation, visualization, and other high-performance benefits using the core scientific packages NumPy, Pandas, SciPy, Sklearn/Scikit learn and Matplotlib. Readers will gain a deep understanding with problem-solving experience on these powerful platforms when dealing with engineering and scientific problems related to Machine Learning and Artificial Intelligence. Several examples of real problems using these techniques are provided along with examples. The authors also focus on the best practices in theindustry on using Python for AI and ML. Deployment on a cloud infrastructure is described in detail (with code) to emphasize real scenarios.
More than a Chatbot: Language Models Demystified by 
Streaming Databases: Unifying Batch and Stream Processing by
Real-time applications are becoming the norm today. But building a model that works properly requires real-time data from the source, in-flight stream processing, and low latency serving of its analytics. With this practical book, data engineers, data architects, and data analysts will learn how to use streaming databases to build real-time solutions. Authors Hubert Dulay and Ralph M. Debusmann take you through streaming database fundamentals, including how these databases reduce infrastructure for real-time solutions. You'll learn the difference between streaming databases, stream processing, and real-time online analytical processing (OLAP) databases. And you'll discover when to use push queries versus pull queries, and how to serve synchronous and asynchronous data emanating from streaming databases. This guide helps you: Explore stream processing and streaming databases Learn how to build a real-time solution with a streaming database Understand how to construct materialized views from any number of streams Learn how to serve synchronous and asynchronous data Get started building low-complexity streaming solutions with minimal setup
Building an API Product: Design, Implement, Release, and Maintain API Products that Meet User Needs by
Unlock the power of APIs with expert guidance and techniques for successfully creating, testing, and maintaining products that align with customers and stand out in the market Key Features Understand the complete API product lifecycle, from planning to implementation, release, and maintenance Design and support API products that distinguish themselves on the market by meeting user needs Implement actionable techniques for developing, testing, and securing a successful API product Purchase of the print or Kindle book includes a free PDF eBook Book Description The exponential increase in the number of APIs is evidence of their widespread adoption by companies seeking to deliver value to users across diverse industries, making the art of building successful APIs an invaluable skill for anyone involved in product development. With this comprehensive guide, you'll walk through the entire process of planning, designing, implementing, releasing, and maintaining successful API products. You'll start by exploring all aspects of APIs, including their types, technologies, protocols, and lifecycle stages. Next, you'll learn how to define an API strategy and identify business objectives, user personas, and jobs-to-be-done (JTBD). With these skills, you'll delve into designing and validating API capabilities to create a machine-readable API definition. As you advance, the book helps you understand how to choose the right language and framework for securely releasing an API server and offers insights into analyzing API usage metrics, improving performance, and creating compelling documentation that users love. Finally, you'll discover ways to support users, manage versions, and communicate changes or the retirement of an API.By the end of this API development book, you'll have the confidence and skills to create API products that truly stand out in the market. What you will learn Master each stage of the API lifecycle Discover technologies and protocols employed in building an API product Understand the different API design definition and validation techniques Generate an API server from a machine-readable definition Understand how to set up and analyze API monitors Familiarize yourself with the different gateways for releasing an API Find out how to create an API portal that attracts users Gain insights into planning and communicating API retirement to users Who this book is for Building an API Product is a guide for product managers and software developers navigating the world of APIs to build programmable products. You don't have to be an experienced professional to learn from this book as long as you have basic knowledge of internet technologies and how users interact with a product. ]]>
Data Mining and Exploration: From Traditional Statistics to Modern Data Science by
Helps readers to transition from traditional statistics to modern data science Reviews the pros and cons of open source and commercial software packages, and their proper applications in specific situations. Explores data using dynamic methods rather than counting on dichotomous thinking. Considers alternate models using ensemble models and model comparison rather than fixing a preconceived hypothesis/model on a single method. Shows how to find the hidden pattern in the data by dynamic visualization rather than over-relying on numeric results.
Large Language Model-Based Solutions: How to Deliver Value with Cost-effective Generative AI Applications by
Learn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject.
