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BECOME AN INSTRUCTOR
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AWS Developer ML

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AWS Developer ML

Duration: 25 hrs to 30 hrs. (6 days with per day 4.5 hrs or 5 days with per day 5 hrs)

Objectives

  • This course is designed to provide comprehensive understanding on AWS Generative AI foundation services with hands-on demo sessions and good overview on LLM, ML and all other AWS services.
  • By the end of this course, you will be able to:
  • Understand various AWS services
  • Understanding on Cloud Compute, Storage, Networking, DB, Security, Deployment and Monitoring services
  • Strong Build ML & LLM services and automate the deployment pipeline and apply security measures
  • Manage, monitor and troubleshoot the cloud infrastructure and cloud application / solutions

Audience

  • Professionals whoever aspire to learn AWS and build / deploy LLM & ML services

Pre-requisite

  • General knowledge on application design, development and hosting is recommended
  • General knowledge on networking concepts, storage concepts and IT infrastructure is recommended

Hardware & Network Requirements

  • TBD

Day 1

Module-1: Intro to AWS (1 hours)

  • Cloud Computing Overview
  • AWS Services Overview
  • AWS Management Console Overview
  • Availability Zones/Regions

Module 2: Cloud Compute Services (1 hours)

  • Cloud Compute Overview
  • Amazon EC2 (Elastic Cloud Compute) Overview
  • Elastic IPs, Security Groups, Key Pairs, Placement Groups
  • EC2 CLI / API

Module-3: AWS Security and IAM (1 hours)

  • Cloud Security Overview
  • AWS Shared Security Model
  • AWS Physical, Hardware, Software, Network Security
  • AWS Security Compliance and Certifications
  • AWS Security Services
  • AWS IAM (Identify Access Management) Overview
  • AWS IAM Elements – User, Group, Role, Policy
  • IAM Best Practices
  • AWS Management Console Security
  • AWS CLI/API Security
  • Multi Factor Authentication
  • Programmatic Access – Access Keys, Secret Access Keys
  • Security Token Service Overview

Hands-on Practical

Labs/Demo:

  • Creating IAM users, roles and groups
  • Configuring and applying permissions for IAM users
  • Creating and applying custom policies
  • Protecting AWS resources with IAM policies / roles

Module-4: Cloud Storage Services (3 hours) - Partial

  • Cloud Storage Overview
  • AWS S3 (Simple Storage Service) Overview
  • EBS Overview, EFS Overview
  • Glacier Overview, Storage Gateway Overview
  • Hands-on Practical Labs/Demo:
  • Creating S3 Bucket and how to upload / download the data o Applying Version Control
  • Enabling Cross Region Replication

Day 2 (contd..)

  • S3 Bucket Lifecycle Management
  • S3 Security & Encryption
  • Static website hosting using S3
  • Working with S3 buckets using AWS CLI / Python SDK

Module 5: Cloud Database Services (3 hours)

  • Database As Service Overview
  • RDS Instance Overview
  • DynamoDB Overview
  • Redshift Overview
  • Elastic Cache Overview – (Opensearch)
  • Aurora Overview

Hands-on Practical Labs/Demo

  • Launching RDS instance with MySQL database engine
  • Launching and working with Dynamo DB
  • Working with S3 buckets using AWS CLI / Python SDK

Module : 6 Cloud Network & CDN Services (1 hours)

  • Cloud Network Overview
  • Amazon VPC (Virtual Private Cloud)
  • Amazon VPN
  • Amazon Direct Connect

Day 3

Module 6: Cloud Network & CDN Services (contd..)

  • CDN (Content Delivery Network) Overview
  • CloudFront Overview

Module 7: Load Balancing and Auto Scaling Services (1 hours)

  • Load Balancers Overview
  • Elastic Load Balancer
  • Auto Scaling Overview

Hands-on Practical Labs/Demo

  • Creating and configuring Elastic Load Balancer to load balance the EC2 instances with sample webapp
  • Creating and configuring Auto Scaling Groups to scale EC2 instances on the fly based on configured rule
  • Working with AWS CLI / Python SDK

Module 8: Cloud Provisioning and Management (2 hours)

  • Infrastructure as a Code Overview
  • CloudFormation
  • Organizations, Resource Groups & Tagging
  • Hybrid Cloud Concepts
  • Terraform Overview
  • Terraform Setup and Configuration
  • Terraform Architecture and Concepts

Hands-on Practical Labs:

  • Creating CloudFormation templates and provision AWS infrastructure o Creating Terraform templates and provision AWS infrastructure

Module 9: AWS Monitoring Services (1 hours) (Optional)

  • CloudWatch
  • CloudTrail
  • X-Ray Service
  • Event and Notification Management

Module 10: Serverless Computing (2 hours)

  • Serverless Computing Overview
  • AWS Lambda, Step Functions
  • Lambda – Best practices, limitations, sample use cases
  • AWS API Gateway

Day 4

  • SQS, SNS Overview

Hands-on Practical Labs/Demo:

  • Implement and deploy serverless application
  • Working with AWS CLI / Python SDK

Module 11: ML as a Service (SageMaker) (4 hours)

  • Ml Services Overview
  • SageMaker Overview and Concepts
  • SageMaker Architecture
  • SageMaker ML Workflow
  • Data Preparation and Exploration
  • Building and Training Models
  • Model Deployment

Hands-on Practical Labs/Demo:

  • Build and deploy sample ML model on SageMaker
  • Working with AWS CLI / Python SDK

Day 5

Module 12: Container Services (3 hours)

  • Docker Overview
  • Kubernetes Overview
  • ECS Overview
  • ECR Overview
  • EKS Overview
  • EKS Architecture and Concepts

Hands-on Practical Labs/Demo:

  • Containerizing sample app and deploy using ECR and EKS o Working with AWS CLI / Python SDK

Module 13: AWS SSO & Other Security Services (1 hours)

  • AWS Single Sign-On (SSO) Overview
  • AWS Directory Service Overview
  • Amazon Cognito Overview
  • AWS Key Management Service (KMS)
  • Protecting Data at rest and in-transit
  • Inspector, Firewall Manager, GuardDuty, WAF & Shield
  • AWS Secrets Manager, Certificate Manager

Hands-on Practical Labs/Demo:

  • Manage access for AWS Accounts and Applications with AWS SSO
  • Encrypting the data stored in the AWS cloud

Module 14: AWS Deployment Services (2 hours)

  • Commit code to a repository and invoke build, test and/or deployment actions
  • Use labels and branches for version and release management
  • Use AWS CodePipeline to orchestrate workflows against different environments
  • Apply AWS CodeCommit, AWS CodeBuild, AWS CodePipeline, AWS CodeStar, and AWS
  • CodeDeploy for CI/CD purposes
  • Perform a roll back plan based on application deployment policy

Hands-on Practical Labs/Demo:

  • Automate CI/CD pipeline for sample ML application

Module 15: Bedrock + GenAI (6 hours)

  • Setting Up Bedrock
  • Request Access
  • Setting up Amazon SageMaker Studio
  • Foundations of Prompt Engineering
  • Familiarize yourself with basic concepts of Amazon Bedrock.

Day 6

Module 15: Bedrock + GenAI (Contd..)

  • Addressing the Challenges of Building Language Models

  • Using Amazon SageMaker for Training Language Models

  • Deploying Language Models (Foundation models)

  • Understand how Amazon Bedrock works.

  • Recognize the benefits of Amazon Bedrock.

  • List typical use cases for Amazon Bedrock.

  • Describe the typical architecture associated with an Amazon Bedrock solution.

  • Understand the cost structure of Amazon Bedrock.

  • Implement a demonstration of Amazon Bedrock in the AWS Management Console.

Hands-on Practical Labs:

  • Case study of using Inferencing the LLM model which is containerized in the previous module.

T. Sanjay

Tech Enthusiast | Seasoned Corporate EnterT(r)ainer

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