Machine Learning Upgrade
A Data Scientist's Guide to Mlops, Llms, and ML Infrastructure
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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 ...
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.
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