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New MEAP! Evaluating AI Systems
AI systems fail in ways you’ve never seen before. A model scores high on benchmarks and still misleads users. An agent completes a task by taking harmful actions and then covering its tracks. A RAG pipeline retrieves the right documents and still returns a fabricated response. Evaluating AI Systems teaches you how to know—and prove—whether your LLM-based applications are ready to ship. In this practical and important book, author Panos Alexopoulos provides a data and decision-driven approach that starts with the questions you need to answer about your system and works backwards to the evidence you need to gather.
Throughout the book, examples and case studies spanning predictive, generative, and agentic AI demonstrate how the same evaluation principles can be adapted to different systems and technologies. By the end, you’ll have the principles, frameworks, and practical judgment to build evaluations that produce evidence you can act on! [Read more]
3 chapters of this new MEAP are available now with more to follow soon!
MicroPython brings the power and familiarity of modern Python to a wide range of devices, from desktop computers and Linux systems to microcontrollers and calculators. This video course teaches you how to use MicroPython to build practical, cross-platform applications through a focused, hands-on discovery process. You’ll learn an evolutionary approach to developing your own projects, discover how to exchange data with office applications, import and export files, and extend your programs with third-party packages. [Read more]
Already know another programming language? This fast-paced video course gets you past the beginner material and straight into what makes modern Python 3 useful. In under two hours, you’ll work with core modules, collections, objects, built-in functions, lambdas, sorting, enumerations, slicing, f-strings, comprehensions, testing, and reporting. You’ll also explore first-class functions, command-line programming, and practical cross-platform DevOps examples. [Read more]
Python has become a key language for DevOps, operations, networking, and security professionals, thanks to its strong support for POSIX-based systems and cross-platform automation. In this course, you’ll build a practical foundation in the Python concepts and tools most useful for software operations. You’ll work with Python’s core operating system modules, collections, objects, and built-in functions while learning how to automate common system tasks. Along the way, you’ll develop essential skills with lambdas, sorting, enumerations, slicing, f-strings, comprehensions, reporting, and testing. [Read more]
Banks, insurance companies, mortgage lenders, and other institutions that deal with big money play by different rules than the rest of the software industry. Financial software—including machine learning and AI models—has to survive contact with regulators, risk committees, and real markets.
In this authoritative guide, author Taehun Kim distills a decade of fintech expertise into a structured 4-Layer Framework encompassing Data Assets, Modeling, Strategy & Monitoring, and Application. Step by step, you’ll discover everything you need to build powerful AI-driven
financial applications capable of addressing four key industry challenges: fraud detection, credit scoring, investment strategy, and personalized forecasting! [Read more]
Embedded Software with Rust >> Chapter 8, “Rust-C Integration and Migration” >> Examine the mechanics of Rust-C integration and migration, including calling C from Rust, calling Rust from C, and automation. Then explore the question that every team eventually asks—and the decisions that need to be made in order to answer it.
Rearchitecting LLMs >> Chapter 11, “Precision pruning for bias” >> One variable is all it takes to change the behavior of a model with otherwise identical prompts. Learn a technique for getting a handle on that specific behavior—without having to retrain the model.
discover the two questions that determine your own system’s exposure. Then, explore how to make decisions while the technology and laws continue to change.
behavior. Then, explore one of the most commercially compelling techniques in applied LLM work as you build a black-box distillation pipeline. Next, see how to teach your model to judge rather than to merely imitate, and dig into a discipline that turns a single deployed model into a sustainable capability to address deteriorating outputs—despite the fact that your model hasn’t changed.
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