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Informationen zum Autor Kristen Kehrer has been providing innovative and practical statistical modeling solutions since 2010. In 2018, she achieved recognition as a LinkedIn Top Voice in Data Science & Analytics. Kristen is also the founder of Data Moves Me, LLC. Caleb Kaiser is a Full Stack Engineer at Comet. Caleb was previously on the Founding Team at Cortex Labs. Caleb also worked at Scribe Media on the Author Platform Team. Klappentext An end-to-end framework for developing Large Language Model (LLM)-based applications Traditionally, there has been a divide between data scientists and software engineers. With the advent of LLMs, however, this has changed. Machine learning is no longer primarily a tool for data analysis, but is now a fundamental feature of modern software applications. In Machine Learning Upgrade , data scientists are given a comprehensive framework not just for understanding LLMs, but for building efficient, reproducible, and scalable LLM applications. Written by leading data scientists, this book brings you up to date on the current state of LLM technology and offers both a conceptual and hands-on overview of how it can be most responsibly integrated into business. Readers will follow along as the authors build an LLM-powered application, providing a concrete example of their framework in action. Best practices for data versioning, experiment tracking, model monitoring, and ethical considerations are also central. Data professionals of all levels looking for a holistic understanding of LLM aplications using the latest technologies and practices will benefit from this book. By adopting a data-centric view, we can identify opportunities to integrate LLMs and drive business success. Inhaltsverzeichnis Introduction ix 1 A Gentle Introduction to Modern Machine Learning 1 Data Science Is Diverging from Business Intelligence 3 From CRISP-DM to Modern, Multicomponent ml Systems 4 The Emergence of LLMs Has Increased ML's Power and Complexity 7 What You Can Expect from This Book 9 2 An End-to-End Approach 11 Components of a YouTube Search Agent 13 Principles of a Production Machine Learning System 16 Observability 19 Reproducibility 19 Interoperability 20 Scalability 21 Improvability 22 A Note on Tools 23 3 A Data-Centric View 25 The Emergence of Foundation Models 25 The Role of Off-the-Shelf Components 27 The Data-Driven Approach 28 A Note on Data Ethics 28 Building the Dataset 30 Working with Vector Databases 34 Data Versioning and Management 50 Getting Started with Data Versioning 53 Knowing "Just Enough" Engineering 57 4 Standing Up Your LLM 61 Selecting Your LLM 61 What Type of Inference Do I Need to Perform? 65 How Open-Ended Is This Task? 66 What Are the Privacy Concerns for This Data? 66 How Much Will This Model Cost? 67 Experiment Management with LLMs 68 LLM Inference 74 Basics of Prompt Engineering 74 In-Context Learning 77 Intermediary Computation 85 Augmented Generation 89 Agentic Techniques 94 Optimizing LLM Inference with Experiment Management 102 Fine-Tuning LLMs 111 When to Fine-Tune an LLM 112 Quantization, QLOrA, and Parameter Efficient Fine-Tuning 113 Wrapping Things Up 121 5 Putting Together an Application 123 Prototyping with Gradio 125 Creating Graphics with Plotnine 128 Adding the Author Selector 137 Adding a Logo 138 Adding a Tab 139 Adding a Title and Subtitle 140 Changing the Color of the Buttons 140 Click to Download Button 141 Putting It All Together 141 Deploying Models as APIs 144 Implementing an API w...
