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Building AI Applications with Databricks and Gen AI
Rating: 3.2 out of 5(18 ratings)
152 students

Building AI Applications with Databricks and Gen AI

Unlock the power of AI with Databricks & GenAI - Transforming data into intelligence, one application at a time!
Created bySagar Prajapati
Last updated 5/2025
English

What you'll learn

  • Use of Vector Search and LLM Models inside Databricks
  • Build Applications and deploy to Databricks Apps
  • Build End - End Data Refresh cycle
  • Automatic refresh data

Course content

7 sections • 27 lectures • 7h 12m total length

  • Introduction5:19

    Build a databricks-based chatbot using vector search and embeddings from hugging face, with a rag architecture and model serving. Create a streamlit app to interact with the data.

  • Understanding of Dataset7:32

    Generate synthetic healthcare data and build a Databricks pipeline. Clean data in silver and gold layers, enable embedding and vector search for a hospital chatbot.

  • Databricks Workspace Setup in AWS13:37
  • Create S3 Bucket2:50

    This lecture shows how to create a dedicated s3 bucket for source data in a Databricks workflow, configure access, and prepare folders for patient, laboratory, appointment, medical, and insurance data.

  • Download Dataset and Github Repo0:02
  • Prepare Source System - Upload Datasets in S3 Bucket8:24

    Upload daily csv datasets to an S3 bucket, structuring folders like patient data, medical history data, and lab results data, then prepare to pull with Auto Loader into Unity Catalog.

Requirements

  • You should be good in Python Programming Language

Description

This comprehensive course will teach you how to develop cutting-edge AI applications by combining the power of Databricks and Large Language Models (LLMs). You will explore how to leverage Databricks for large-scale data processing, feature engineering, and model training, while integrating advanced LLMs for natural language processing (NLP) tasks such as text classification, summarization, semantic search, and conversational AI.

Through hands-on labs and real-world projects, you will gain practical experience in building intelligent systems that can understand, process, and generate human language. This course is ideal for data scientists, machine learning engineers, and developers who want to stay ahead in the rapidly evolving world of AI.

By the end of the course, you will have a strong understanding of how to architect end-to-end AI pipelines using Databricks and LLMs, deploy scalable NLP applications, and apply industry best practices for model integration and performance optimization.

Key Highlights:

  • Scalable data processing and ML using Databricks

  • NLP-powered applications with state-of-the-art LLMs

  • Practical, project-based learning approach

  • Real-world AI use cases and deployment strategies

  • Use Vector Search indexes to store indexes

  • Use workflows to refresh the data end - end on schedule basis

  • Use Serverless compute to refresh the data

  • Use Databricks Apps to deploy the application

Who this course is for:

  • Intermediate Databricks Engineer

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