Udemy - Integration And Deployment Of Genai Models by Navid Shirzadi, Ph.D

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Free Download Udemy - Integration And Deployment Of Genai Models by Navid Shirzadi, Ph.D
Published 5/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.09 GB | Duration: 8h 9m
Deploy machine learning and generative AI models using AWS SageMaker, Bedrock, and Anthropic Claude.

What you'll learn
How to set up and navigate the AWS Management Console for machine learning projects.
How to build, train, and deploy ML models using Amazon SageMaker Studio and SageMaker Canvas (no-code and low-code environments).
How to optimize machine learning deployment using SageMaker's Hardwire-Optimized Deployment strategies.
How to use SageMaker JumpStart to quickly launch pre-built ML solutions.
How to interact with SageMaker services programmatically using the Python SDK.
How to use Amazon Bedrock for fully managed, serverless hosting of foundation models (FMs).
How to build generative AI applications (text and image generation) using Bedrock.
How to compare SageMaker and Bedrock for different machine learning and generative AI use cases.
How to implement Retrieval-Augmented Generation (RAG) and fine-tune large language models using Bedrock.
How to work with Anthropic Claude models through the cloud and APIs for text generation, role-based assistance, and multimodal tasks (including image recognitio
How to set up and use the Anthropic API to deploy intelligent AI-powered applications.
Requirements
Basic understanding of machine learning concepts (no need for deep expertise).
Familiarity with Python programming (basic to intermediate level).
An AWS account
Description
Unlock the full power of AWS to deploy Machine Learning and Generative AI solutions!In this hands-on course, you'll learn how to use AWS SageMaker, Amazon Bedrock, and Anthropic Claude models to build, train, and deploy intelligent applications.We'll start with setting up your AWS environment and mastering SageMaker's capabilities, from no-code tools like SageMaker Canvas to coding solutions using the Python SDK. You'll then dive into Amazon Bedrock to work with foundation models (FMs) for text and image generation, and implement Retrieval-Augmented Generation (RAG) techniques.Finally, you'll explore Anthropic Claude - learning how to generate text, use role-based AI assistants, and build multimodal (text + image) applications through APIs.Throughout the course, you'll work on real-world projects including text generation, image generation, and fine-tuning large language models.By the end of this course, you will be confident in setting up, managing, and deploying machine learning and AI models using AWS services - whether you are a data scientist, AI developer, cloud engineer, or tech enthusiast.Key Topics Covered:AWS Account Setup and SageMaker Studio EnvironmentNo-Code ML Model Building with SageMaker CanvasModel Deployment with Canvas and SageMaker SDKUsing Amazon Bedrock for Fully Managed Foundation ModelsComparing SageMaker vs. Bedrock for AI DeploymentsBuilding AI Projects: Text Generation, Image Generation, RAG Fine-TuningWorking with Anthropic Claude for API-based Text and Image ApplicationsNo prior cloud deployment experience is required - just basic Python knowledge and a passion for machine learning and AI!
Overview
Section 1: Introduction
Lecture 1 Course Content
Lecture 2 Course Information
Lecture 3 Resources
Section 2: SageMaker (Hardwire) - Optimized ML Deployment
Lecture 4 Setting Up Your AWS Account and Console
Lecture 5 Exploring the SageMaker Console
Lecture 6 Creating Domains, Roles, and User Profiles
Lecture 7 Setting Up the SageMaker Studio Environment
Lecture 8 Introduction to SageMaker Canvas - DataWrangler
Lecture 9 Using Canvas Data Wrangler for Data Preparation
Lecture 10 Building Machine Learning Models with Canvas-Part1
Lecture 11 Building Machine Learning Models with Canvas-Part2
Lecture 12 Stop Running Instances
Lecture 13 Getting Started with SageMaker JumpStart
Lecture 14 SageMaker through Python SDK-Part1
Lecture 15 SageMaker through Python SDK-Part2
Lecture 16 SageMaker through Python SDK-Part3
Section 3: Amazon Bedrock
Lecture 17 Introduction to Amazon Bedrock
Lecture 18 Bedrock Configuration
Lecture 19 Set up AWS Credentials
Lecture 20 Project 1: Text Generation with Bedrock-Part1
Lecture 21 Project 1: Text Generation with Bedrock-Part2
Lecture 22 Project 2: Image Generation with Bedrock-Part1
Lecture 23 Project 2: Image Generation with Bedrock-Part2
Lecture 24 Introduction to Retrieval-Augmented Generation (RAG)
Lecture 25 Project 3: Fine-Tuning an LLM Using RAG
Section 4: Anthropic Claude
Lecture 26 Introduction to Anthropic Claude Models
Lecture 27 Setting Up the Anthropic API
Lecture 28 Project 1: Text Generation with Claude
Lecture 29 Project 2: Using the Assistant Role for AI Applications
Lecture 30 Project 3: Using System Prompt
Lecture 31 Project 4: Multimodal AI (Image Recognition with Claude)
Data scientists, ML engineers, and AI practitioners,Developers and software engineers,Cloud engineers and architects interested,Beginners to intermediate learners who want a guided, hands-on approach to setting up ML and Generative AI projects on AWS.,Professionals preparing for roles in AI/ML deployment, cloud-based AI application development, or AWS AI/ML certifications.

Homepage

https://www.udemy.com/course/integration-and-deployment-of-genai-models-l/

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