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Practical LLM Evaluation for Production Systems. Measure, monitor, and improve AI system reliability across training and inference Ammar Mohanna, Indrajit Kar, Zonunfeli Ralte

Książka w języku angielskim angielski
Practical LLM Evaluation for Production Systems. Measure, monitor, and improve AI system reliability across training and inference Ammar Mohanna, Indrajit Kar, Zonunfeli Ralte - okladka książki

Practical LLM Evaluation for Production Systems. Measure, monitor, and improve AI system reliability across training and inference Ammar Mohanna, Indrajit Kar, Zonunfeli Ralte - okladka książki

Autorzy:
Ammar Mohanna, Indrajit Kar, Zonunfeli Ralte
Serie wydawnicze:
Beginners Guide
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
486
Dostępne formaty:
     PDF
     ePub

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Do przechowalni

Modern 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.

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O autorach książki

Indrajit Kar comes with 18 years of various Industry experience, leading all three division, AI consulting R&D and solution engineering. He and his team build cutting edge AI and deep learning solutions to address some of the toughest problems for his customers.
He has 14 research papers and 12 patents in NLP, Timeseries, Computer Vision, and Deep learning.
In his spare time, Indrajit enjoys giving advice to small and medium-sized entrepreneurs on how to enter the AI and data science markets, attract customers, develop their products, and monetize their existing data. He's won many accolades in his career from ace innovator, services excellence awards, and 40 top data scientist under the age of 40 award.
He has enabled AI & Data science program for sectors like Smart Cities, Retail, supply chain, automotive factories, Healthcare, pharma, infrastructure & utilities. Also heading research and development in the area of Deep learning, predictive maintenance using IIoT/sensor data, edgeAi, Lidar tech, NLP and GPU powered computer vision.
In the past, he spearheaded complex Analytics projects helping industries like BFSI, Retail, CPG, FMCG, petroleum/oil & gas, to take data driven decision, predict business outcomes, allocate budget, predict customer behaviour, retention customers, acquire new customers, maximize revenue & forecasting for key areas Pricing, marketing, sale, advertisement and promotion.
Zonunfeli Ralte is an Artificial Intelligence entrepreneur, researcher, and technology leader. She founded RastrAI Private Limited, the first AI startup from India's North East region, advancing innovation in emerging technologies. Recognized as Mizoram's first woman specializing in Artificial Intelligence and Machine Learning, she has authored three books on Artificial Intelligence, Generative AI, and Computer Vision.
She is also an accomplished researcher with 16 published research papers and six Best Research Awards, reflecting her significant contributions to Artificial Intelligence, Deep Learning, and applied AI innovation.

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