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MLOps for those in a hurry to get stuff done.

Prompt Engineering for LLMs provides a solid foundation of LLM principles and explains how to apply them in practice. When first integrating LLMs into workflows, most developers struggle to coax useful insights from them. That’s because communicating with AI is different from communicating with humans. This guide shows you how to present your problem in the model-friendly way called prompt engineering.

This video series interviews the experts at MLOps to learn how to use MLOps to build and deploy ML models.

AI continues to be a key force driving innovation in the financial ecosystem—automating processes, personalizing customer experiences, mitigating risk, fighting fraud, and predicting financial markets like never before. Join us and leading industry experts to learn more about next-generation AI tools and techniques at the forefront of transformation in financial services.

O’Reilly’s learning platform offers more breadth and depth of content and features to help your tech teams stay ahead.

You’ll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you’ll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images.

Learn how to use Python and today’s most useful DevOps tools to take on everyday Linux systems administration tasks, with these picks from Noah Gift.

Get the edge you need to successfully study for and pass the Certified Kubernetes Administrator (CKA) exam in this all-new video course. This 3rd edition acts as a complete study resource to help you build skills as you work your way through the course. Explore each exam objective on the CKA exam and learn how to build, run, and manage Kubernetes clusters. Expert trainer and author Sander van Vugt gives you hands-on labs to work with Kubernetes, so you have the opportunity to test your skills. The course then ends with a sample exam, allowing you to practice before taking the real thing.

John Berryman takes a deep dive into the problem space and Eventbrite’s approach. He explores how the company gathered training data from its search and click logs, and how it built and refined the model. You’ll see the output of the model and both the positive results of Eventbrite’s work, as well as the work left to be done. You’ll leave with some new ideas to take back to your business.

Learn Kubernetes and then get certified! This playlist will teach you how to run orchestrated containers in the cloud or on premise. Go from basic concepts to hands-on experience and then get your CKAD and CKA certs. This playlist leads you from basics to advanced techniques.

Many organizations today have begun to modernize their Windows workloads to take full advantage of cloud economics. If you’re a C# developer at one of these companies, you need options for rehosting, replatforming, and refactoring your existing .NET Framework applications. This practical book guides you through the process of converting your monolithic application to microservices on AWS.

There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.

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In this four-part course, expert and CKAD Benjamin Muschko walks you through all the topics covered in the exam to fully prepare you to pass with flying colors.

This collection of 19 interactive labs provides hands-on practice in Kubernetes for readers of Kubernetes Patterns, second edition.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how._x000D_- _x000D_- By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

This collection of 37 interactive labs provides hands-on training that enhances the exam prep in the forthcoming Certified Kubernetes Security Specialist (CKS) Study Guide, which takes you through the topics you need to understand for the CKS exam.

“Fundamentals of DevOps and Software Delivery” is a guide to DevOps and software delivery: that is, a guide to the numerous tools and techniques that are required to take that application code and run it and maintain it in production, where it can generate value for your users and your company on an ongoing basis. This includes going through all the modern practices for deploying applications and microservices to the cloud, managing your infrastructure as code, automating your software delivery lifecycle in a CI/CD pipeline, configuring networking, setting up data stores, and hooking up monitoring.

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“Deep Learning at Scale” (O’Reilly) by Suneeta Mall illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it’s effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently.

Acing the Certified Kubernetes Administrator Exam is your fast-track to becoming a Certified Kubernetes Administrator! Your expert exam tutor is Chad Crowell, whose courses have helped thousands of developers to understand Kubernetes and earn the coveted CKA certification. If you’re familiar with Kubernetes, this book will ensure you’re ready to pass in just one month of study. If you’re brand new, this is the perfect primer to get started on your Kubernetes journey.

The Replacement of Organic Search with Advertising by Google and Amazon and What That Might Mean for the Future of AI

Written by Elecia White, an expert who has created systems ranging from DNA scanners to children’s toys, Making Embedded Systems, 2nd Edition from O’Reilly, is ideal for intermediate and experienced programmers, no matter what platform you use. This expanded second edition includes new chapters on IoT and networked sensors.

Gruntwork cofounder Yevgeniy (Jim) Brikman takes you through code examples that demonstrate Terraform’s simple, declarative programming language for deploying and managing infrastructure with a few commands. Veteran sysadmins, DevOps engineers, and novice developers will quickly go from Terraform basics to running a full stack that can support a massive amount of traffic and a large team of developers.

This book is the “Hello, World” tutorial for building products, technologies, and teams in a startup environment. It’s based on the experiences of the author, Yevgeniy (Jim) Brikman, as well as interviews with programmers from some of the most successful startups of the last decade, including Google, Facebook, LinkedIn, Twitter, GitHub, Stripe, Instagram, AdMob, Pinterest, and many others.

Over the past year, LLMs have become “good enough” for real-world applications.

Learn how to use MLflow for managing the machine learning lifecycle. Track experiments, package models, and deploy to production.

The landscape of open source technologies for building AI applications has expanded rapidly since the advent of LLMs and other frontier AI models. But there are ongoing discussions about what constitutes open source AI, and current technologies come with a range of limitations and benefits. Proprietary models may be optimal in some cases, but concerns about cost, privacy, and security for production-grade applications may require a closer look at open source options._x000D_- Join us to explore the features and capabilities of the latest open source AI models, frameworks, and platforms, from Mistral and LangChain to Hugging Face. You’ll learn how to leverage these and other open source AI tools to build more robust and cost-effective AI applications, right from the industry experts already putting them to work in the field.

Over the past year, LLMs have become “good enough” for real-world applications.

O’Reilly Radar article –Last month, The New York Times claimed that tech giants OpenAI and Google have waded into a copyright gray area by transcribing the vast volume of YouTube videos and using that text as additional training data for their AI models despite terms of service that prohibit such efforts and copyright law that the Times argues places them in dispute.