Zonunfeli Ralte – författare

Build reliable Build reliable AI evaluation frameworks that measure quality, safety, grounding, and production readiness across modern LLM and SLM applicationsFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesDesign evaluation frameworks for LLMs, SLMs, multimodal, reasoning, and agentic AI systemsMeasure quality, safety, grounding, robustness, and production readiness with practical metricsApply unified evaluation methods to text, multimodal, and agentic AI systemsBook DescriptionModern AI systems are expected to do far more than generate fluent text. They should be able to retrieve information, reason through complex problems, understand images and documents, call external tools, execute workflows, and support critical business decisions. Evaluating these systems requires methods that go beyond traditional NLP benchmarks.Taking a product-first approach, this book presents evaluation as a continuous operational capability spanning training, inference, and end-to-end system operation. You'll learn how to connect evaluation metrics directly to deployment gates, rollback criteria, monitoring systems, and production reliability objectives.Using practical examples and real-world workflows, you'll explore evaluation strategies for text LLMs, vision-language models, multimodal conversational systems, mixture-of-experts architectures, reasoning models, agentic systems, retrieval pipelines, Text2SQL and Text2Cypher systems, embedding models, OCR workflows, and guardrail SLMs. You'll also learn how to manage non-determinism, design repeatable test suites, validate tool execution, and measure long-horizon agent behavior in production.By the end of the book, you'll be able to design robust evaluation systems that help teams deploy reliable, safe, and economically viable LLM-powered applications with confidence.*Email sign-up and proof of purchase requiredWhat you will learnDesign repeatable evaluation pipelines for LLM systemsAssess inference quality, latency, and operational costEvaluate multimodal, agentic, and reasoning AI systemsBuild regression gates and deployment evaluation workflowsDetect hallucinations and grounding failures in VLMsAssess routing stability in mixture-of-experts modelsEvaluate Text2SQL, OCR, and retrieval-based systemsTranslate evaluation signals into production decisionsWho this book is forML engineers, GenAI engineers, AI architects, data scientists, platform engineers, and engineering managers responsible for deploying LLM-powered systems in production will benefit from this book. Applied AI researchers and technical decision-makers looking to measure reliability, safety, and operational readiness across modern AI systems will also find it valuable. Readers should have a working understanding of machine learning, Python, and modern LLM concepts.

This book researches the intricate world of generative Artificial Intelligence, offering readers an extensive understanding of various components and applications in this field. The book begins with an in-depth analysis of generative models, providing a solid foundation and exploring their combination nuances. It then focuses on enhancing TransVAE, a variational autoencoder, and introduces the Swin Transformer in generative AI. The inclusion of cutting edge applications like building an image search using Pinecone and a vector database further enriches its content. The narrative shifts to practical applications, showcasing GenAI's impact in healthcare, retail, and finance, with real-world examples and innovative solutions. In the healthcare sector, it emphasizes AI's transformative role in diagnostics and patient care. In retail and finance, it illustrates how AI revolutionizes customer engagement and decision making.

This book explores cutting-edge computer vision techniques, starting with zero-shot and few-shot learning, DETR, and DINO for object detection. It covers advanced segmentation models like Segment Anything and Vision Transformers, along with YOLO and CLIP. Using PyTorch, readers will learn image regression, multi-task learning, multi-instance learning, and deep metric learning. Hands-on coding examples, dataset preparation, and optimization techniques help apply these methods in real-world scenarios. Each chapter tackles key challenges, introduces architectural innovations, and improves performance in object detection, segmentation, and vision-language tasks.