Search results for "Quick Start Guide to Large Language Models (LLMs)"
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Designing Large Language Model Applications
Transformer-based language models are powerful tools for solving a variety of language tasks and represent a phase shift in the field of natural language processing. But the transition from demos and prototypes to full-fledged applications has been slow. With this book, you'll learn the tools, techniques, and playbooks for building useful products that incorporate the power of language models.Experienced ML researcher Suhas Pai provides practical advice on dealing with commonly observed failure modes and counteracting the current limitations of state-of-the-art models. You'll take a comprehensive deep dive into the Transformer architecture and its variants. And you'll get up-to-date with the taxonomy of language models, which can offer insight into which models are better at which tasks.You'll learn:Clever ways to deal with failure modes of current state-of-the-art language models, and methods to exploit their strengths for building useful productsHow to develop an intuition about the Transformer architecture and the impact of each architectural decisionWays to adapt pretrained language models to your own domain and use casesHow to select a language model for your domain and task from among the choices available, and how to deal with the build-versus-buy conundrumEffective fine-tuning and parameter efficient fine-tuning, and few-shot and zero-shot learning techniquesHow to interface language models with external tools and integrate them into an existing software ecosystem
R 1,824.00
Developer's Playbook for Large Language Model Security
Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models.Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list—a feat accomplished by more than 400 industry experts—this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI.You'll learn:Why LLMs present unique security challengesHow to navigate the many risk conditions associated with using LLM technologyThe threat landscape pertaining to LLMs and the critical trust boundaries that must be maintainedHow to identify the top risks and vulnerabilities associated with LLMsMethods for deploying defenses to protect against attacks on top vulnerabilitiesWays to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization
R 1,824.00
Building Machine Learning Systems with a Feature Store
Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems.Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML—the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems.With this book, you'll be able to:Make the leap from training ML models to building ML systemsDevelop an ML system as modular feature, training, and inference pipelinesDesign, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generationLearn the problems a feature store for ML solves when building ML systemsUnderstand the principles of MLOps for developing and safely updating ML systemsJim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden.
R 1,978.00
