Large Language Models: Application through Production

مقدمة من

شعار المنصة
غير متاح
42.00 ساعة تعليمية
متوسط
اللغة :
الإنجليزية
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نبذة عن المقرر

This course is aimed at developers, data scientists, and engineers looking to build LLM-centric applications with the latest and most popular frameworks. You will use Hugging Face to solve natural language processing (NLP) problems, leverage LangChain to perform complex, multi-stage tasks, and deep-dive into prompt engineering. You will use data embeddings and vector databases to augment LLM pipelines. Additionally, you will fine-tune LLMs with domain-specific data to improve performance and cost, as well as identify the benefits and drawbacks of proprietary models. You will assess societal, safety, and ethical considerations of using LLMs. Finally, you will learn how to deploy your models at scale, leveraging LLMOps best practices.

By the end of this course, you will have built an end-to-end LLM workflow that is ready for production!

المدربين

Matei Zaharia

Matei Zaharia is a Cofounder and Chief Technologist at Databricks as well as an Assistant Professor of Computer Science at UC Berkeley. He started the Apache Spark project during his PhD at UC Berkeley in 2009, and has worked broadly on other widely used data and machine learning software, including MLflow, Delta Lake and Apache Mesos. He works on a wide variety of projects in data management and machine learning at Databricks and Stanford. Matei’s research was recognized through the 2014 ACM Doctoral Dissertation Award, an NSF CAREER Award, and the US Presidential Early Career Award for Scientists and Engineers (PECASE).

Sam Raymond

Sam Raymond is a Senior Data Scientist in the Machine Learning Practice at Databricks. Sam received his PhD in Computational Engineering and Machine Learning at MIT. Prior to joining Databricks he spent several years developing courseware on digital transformation and publishing research papers in areas such as bioengineering, climate and sustainability, and simulation as a postdoctoral researcher in deep learning and data science at Stanford University and MIT.

Chengyin Eng

Chengyin Eng is a Senior Data Scientist in the Machine Learning Practice at Databricks. She is experienced in developing and productionizing scalable machine learning solutions for cross-functional clients. She received her Master’s in Computer Science, specializing in Data Science, from University of Massachusetts, Amherst. She has spoken at various machine learning conferences and meetups, including Open Data Science, PyData, Data + AI, and MLOps World.

Joseph Bradley

Joseph Bradley works as a Lead Solutions Architect at Databricks, specializing in Machine Learning, and is an Apache Spark committer and PMC member. Previously, he was a Staff Software Engineer at Databricks and a postdoc at UC Berkeley, after receiving his Ph.D. in Machine Learning from Carnegie Mellon.