Databricks to Local LLMs

Duke University via Coursera

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Overview

"Databricks to Local LLMs" is an advanced course aimed at data scientists, machine learning engineers, and AI developers interested in building, training, and deploying large language models (LLMs) using Databricks and transitioning them for local or on-premise use. This course provides a full pipeline overview—from data preparation and model training on Databricks to optimizing and running LLMs in local environments. Learners begin with using Databricks’ unified analytics platform for distributed training, leveraging Delta Lake and MLflow for efficient experimentation and tracking....

Syllabus

Databricks Lakehouse Platform Fundamentals

In this module, you will learn how to describe the Databricks architecture, create clusters, use notebooks for analysis, and share notebooks by completing hands-on labs and knowledge checks on these topics.

Data Transformation and Pipelines

In this module, you will learn how to read and transform data, create Delta Lake pipelines, and work with complex data types by implementing ETL solutions and passing code samples reviews.

Responsible Generative AI

In this module, you will learn foundations of generative AI and responsible deployment strategies to benefit from the latest advancements while maintaining safety, accuracy, and oversight.By directly applying concepts through hands-on labs and peer discussions, you will gain practical experience putting AI into production.

Local LLMOps

In this module, you will learn mitigation strategies, evaluate task performance, and operationalize workflows by identifying risks in notebooks and deploying an LLM application.