Aws Certified Ai Practitioner AifC01

Aws Certified Ai Practitioner – Aif-C01
Published 2/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Prepare yourself for the AWS Certified AI Practitioner certification exam

What you’ll learn

Students will familiarize with services such as Amazon SageMaker, Bedrock and other services related to the field of Machine Learning and AI

Students will gain foundation knowledge when it comes to Generative AI.

Students will be better prepared to attempt the AWS Certified AI Practitioner exam.

Requirements

Description

Overview

Section 1: Introduction

Lecture 1 How has the course been structured

Lecture 2 Introduction to Cloud Computing

Lecture 3 Using Amazon Web Services as a cloud service

Lecture 4 Lab – Creating an AWS Account

Lecture 5 Accessing your AWS Account

Lecture 6 Our first AWS service – Amazon S3

Lecture 7 Lab – Working with Amazon S3

Lecture 8 Review of Amazon S3

Section 2: Let’s work on Machine Learning

Lecture 9 Understanding different terms

Lecture 10 Considering Machine Learning

Lecture 11 Broad-level understanding of the Machine Learning process

Lecture 12 Data – The star of the show

Lecture 13 Different types of data

Lecture 14 Different types of Machine Learning tasks

Lecture 15 Amazon SageMaker AI

Lecture 16 Quick Intro on different compute options

Lecture 17 Lab – Building an EC2 Instance

Lecture 18 Lab – Connecting to the EC2 Instance

Lecture 19 A note on the costing aspect

Lecture 20 Lab – Creating an Amazon SageMaker domain

Lecture 21 Quick tour of Amazon SageMaker Studio

Lecture 22 Our data set

Lecture 23 Lab – Launching SageMaker Canvas

Lecture 24 Lab – Amazon Canvas – Data Wrangler – Ingesting our data

Lecture 25 Lab – Amazon Canvas – Data Wrangler – Data Insights

Lecture 26 Lab – Amazon Canvas – Data Wrangler – Transforming data

Lecture 27 Lab – Amazon Canvas – Training the Model

Lecture 28 Lab – Amazon Canvas – Making predictions

Lecture 29 Amazon Canvas – Analyzing results

Lecture 30 Amazon SageMaker feature store

Lecture 31 Gotcha’s when using training data

Lecture 32 Amazon SageMaker – Using the ready-to-use models

Lecture 34 Amazon SageMaker Clarify

Lecture 35 Amazon SageMaker Ground Truth

Lecture 36 Synthetic data

Lecture 37 Different use cases for usage of Machine Learning

Lecture 38 Principles of Response AI

Lecture 39 Overview on MLOps

Lecture 40 Machine Learning Lifecycle – AWS Well-Architected Framework

Section 3: AWS Managed AI services

Lecture 41 Using the inbuilt AWS AI services

Lecture 42 Amazon Comprehend

Lecture 43 Lab – Using the Amazon Comprehend service

Lecture 44 Amazon Textract

Lecture 45 Lab – Using the Amazon Textract service

Lecture 46 Amazon Transcribe

Lecture 47 Lab – Using Amazon Transcribe

Lecture 48 Amazon Rekognition

Lecture 49 Lab – Using Amazon Rekognition

Lecture 50 Amazon Polly

Lecture 51 Lab – Using Amazon Polly

Lecture 52 Amazon Translate

Lecture 53 Lab – Amazon Translate

Lecture 54 Amazon Forecast

Lecture 55 Amazon Lex

Lecture 56 Lab – Using Amazon Lex

Lecture 57 Amazon Personalize

Lecture 58 Amazon Comprehend Medical

Lecture 59 Amazon Kendra

Section 4: Generative AI

Lecture 60 Large Language Models

Lecture 61 What is a Foundation Model

Lecture 62 Introduction to Generative AI

Lecture 63 A look at using ChatGPT

Lecture 64 Anthropic Claude

Lecture 65 Stable Diffusion

Lecture 66 Hugging Face

Lecture 67 Meta Llama

Lecture 68 What is Amazon Bedrock

Lecture 69 Lab – Amazon Bedrock – Requesting access to models

Lecture 70 Amazon Bedrock – Using Amazon Titan Model

Lecture 71 Amazon Bedrock – Using Amazon Titan Image Generator

Lecture 72 Amazon Bedrock – Inference parameters

Lecture 73 Prompt Engineering

Lecture 74 Prompt Engineering – Be clear

Lecture 75 Prompt Engineering – Different types of prompts

Lecture 76 Prompt Engineering – Using system prompts

Lecture 77 Prompt Engineering – Passing data and instructions

Lecture 78 Prompt Engineering – Prompt Templates

Lecture 79 Prompt Engineering – Resources

Lecture 80 When to choose what model

Lecture 81 Evaluating Foundation Models

Lecture 82 Customizing foundation models

Lecture 83 Amazon Q Developer

Lecture 84 Lab – Amazon RDS Aurora – Launching an instance

Lecture 85 Lab – Amazon RDS Aurora – Connecting to the database

Lecture 86 Lab – Amazon RDS Aurora – Connecting to the database – Resources

Lecture 87 What is Amazon OpenSearch

Lecture 88 What is RAG – Retrieval Augmented Generation

Lecture 89 Amazon Bedrock – Knowledge base – Chat with your document

Lecture 90 Lab – Amazon Bedrock – Knowledge Base – Implementation Overview

Lecture 91 Lab – Amazon Bedrock – Knowledge Base – Creating an IAM user

Lecture 92 Lab – Amazon Bedrock – Knowledge Base – Implementation

Lecture 93 Challenges on using Generative-AI

Lecture 94 Amazon Bedrock Guardrails

Lecture 95 Lab – Amazon Bedrock Guardrails

Lecture 96 Amazon Bedrock Agents

Lecture 97 More on Amazon Bedrock pricing

Section 5: Security and Monitoring on AWS

Lecture 98 Identity and Access Management

Lecture 99 IAM Users and Groups

Lecture 100 AWS Key Management service and Amazon Bedrock

Lecture 101 What is Amazon CloudWatch

Lecture 102 Amazon Bedrock and Amazon CloudWatch

Lecture 103 Lab – Amazon Bedrock and Amazon CloudWatch

Lecture 104 What is AWS CloudTrail

Lecture 105 Amazon Bedrock – AWS PrivateLink

Lecture 106 Amazon SageMaker and network isolation

Lecture 107 Amazon Macie

Lecture 108 AWS Config

Lecture 110 AWS Audit Manager

Lecture 111 AWS Trusted Advisor

Lecture 112 Quick note on the design of a conversational chatbot

Lecture 113 Securing your Gen-AI applications

Lecture 114 Generative AI Security Scoping Matrix

Section 6: Practice Tests