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Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.40 GB | Duration: 7h 12m
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.40 GB | Duration: 7h 12m
Unlock the power of AI with Databricks & GenAI - Transforming data into intelligence, one application at a time!
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
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 DatabricksNLP-powered applications with state-of-the-art LLMsPractical, project-based learning approachReal-world AI use cases and deployment strategiesUse Vector Search indexes to store indexesUse workflows to refresh the data end - end on schedule basisUse Serverless compute to refresh the dataUse Databricks Apps to deploy the application
Overview
Section 1: Architecture and Prerequiste
Lecture 1 Introduction
Lecture 2 Understanding of Dataset
Lecture 3 Databricks Workspace Setup in AWS
Lecture 4 Create S3 Bucket
Lecture 5 Download Dataset
Lecture 6 Prepare Source System - Upload Datasets in S3 Bucket
Section 2: Ingest Data from S3 to Unity Catalog
Lecture 7 Create Schema
Lecture 8 Understand Groups and Permission
Lecture 9 How to get access on S3 bucket and Create Scopes in AWS Databricks
Lecture 10 Ingest Data from S3 to Bronze Layer
Lecture 11 Create Repo and Link in Databricks
Lecture 12 Ingest other data into Bronze Layer and Setup Workflow
Section 3: Cleaning the bronze data
Lecture 13 Build Lakehouse Quality Dashboard Manually
Lecture 14 Create Monitor for all the Bronze Tables - Automation
Lecture 15 Create Clean Notebook for Patient Data
Lecture 16 Create Clean Notebook for Other Tables - Optimise
Lecture 17 Create Clean Notebook for Other Tables
Lecture 18 Update Workflow for Silver Notebooks
Section 4: Ingest data into Gold Layer
Lecture 19 Create Dimension Master Table Gold Layer and Add in Workflows
Section 5: Vector Search and Embedding Models
Lecture 20 Create Vector Search Endpoint
Lecture 21 Create Embedding Model and Serving EndPoint
Lecture 22 Create Embeddings and Create VS Index table
Lecture 23 Update Workflow - Add VS Notebooks
Section 6: Setup Model Serving Endpoint
Lecture 24 Create Serving Endpoint with RAG
Lecture 25 Query serving endpoint
Lecture 26 Run Workflows using SPN
Section 7: Build Streamlit Chatbot App
Lecture 27 Create Streamlit ChatBot Application
Intermediate Databricks Engineer
Screenshots
Download link
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