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Practical MLOps for Data Scientists & DevOps Engineers – AWS

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Practical MLOps for Data Scientists , Machine Learning & DevOps Engineers - Implement MLOps - Deploy Models and Operate


1. About AWS MLOps Course and Instructor
  • 1. About the MLOps with AWS Course
  • 2. How to make the most of this course
  • 3. Source Code of this course.html

  • 2. Introduction to MLOps
  • 1. What & Why MLOps
  • 2. Quick Hands On Demo on MLOps
  • 3. MLOps Fundamentals
  • 4. MLOps Fundamentals - Deep Dive
  • 5. Why DevOps alone is not Suitable for Machine Learning
  • 6. What is AWS & its Benefits
  • 7. Technical Stack of AWS for MLOps & Machine Learning

  • 3. DevOps for Data Scientists
  • 1. What is SDLC & Why its Important
  • 2. Types of SDLC
  • 3. Waterfall Vs Agile Vs DevOps
  • 4. DevOps Lifecycle & Tools in AWS

  • 4. Getting Started with AWS
  • 1. What do we cover in this section
  • 2. Create AWS Account
  • 3. Setting up MFA on Root Account
  • 4. Create IAM Account and Account Alias
  • 5. Setup CLI with Credentials
  • 6. IAM Policy
  • 7. IAM Policy generator & attachment
  • 8. Delete the IAM User
  • 9. S3 Bucket and Storage Classes
  • 10. Creation of S3 Bucket from Console
  • 11. Creation of S3 Bucket from CLI
  • 12. Version Enablement in S3
  • 13. Introduction EC2 instances
  • 14. Launch EC2 instance & SSH into EC2 Instances
  • 15. Clean Up Activity

  • 5. Linux Operating System for DevOps and Data Scientists
  • 1. What do we learn in this section
  • 2. Linux Features & Bash
  • 3. How to Launch EC2 Instances (Quick Refresh)
  • 4. Linux Basic Commands

  • 6. Source code Management using GIT - CodeCommit
  • 1. Introduction to CI CD Pipeline
  • 2. Introduction to AWS Code Commit & DVCS
  • 3. Git Initial config & Git Commands
  • 4. Setting up the workspace for Git
  • 5. Git Workflow
  • 6. Adding files to Staging Area
  • 7. Staged Differences
  • 8. Git Unstage
  • 9. Git Reset & Revert
  • 10. AWS Code Commit Remote Git Commands
  • 11. Cloning and Branching
  • 12. Git Branching Hands On Part 1
  • 13. Git Branching Hands On Part 2
  • 14. Git Conflicts & Resolving them
  • 15. Git Rebase Vs Git Merge
  • 16. Git Stash Introduction
  • 17. Git Stash Hands On
  • 18. AWS Code Commit Security
  • 19. AWS Code Commit Security - Hands On
  • 20. AWS Code Commit Integration - Triggers - Notifications - CloudWatch - EventBridg
  • 21. Summary

  • 7. YAML Crash Course
  • 1. YAML Crash Course

  • 8. AWS CodeBuild
  • 1. Introduction to AWS CodeBuild
  • 2. Create First CodeBuild Project
  • 3. buildspec.yml deep dive
  • 4. Code Build Hands On
  • 5. Environment Variables in CodeBuild & buildspec.yml deep dive Hands On
  • 6. Working CodeBuild Artifacts Hands On
  • 7. AWS CodeBuild Triggers
  • 8. CleanUp Activity

  • 9. AWS Code Deploy
  • 1. AWS CodeDeploy Introduction
  • 2. First AWS CodeDeploy - Intro to Hands On
  • 3. First AWS CodeDeploy
  • 4. appspec.yml - Deep Dive
  • 5. CodeDeploy Summary

  • 10. Code Pipeline
  • 1. AWS CodePipeline Introduction
  • 2. Create CodePepeline - Hands On
  • 3. Automatic CI CD Process with Manual Approval
  • 4. Summary & CleanUp

  • 11. Docker Containers
  • 1. Introduction to Docker
  • 2. Installation of Docker Desktop
  • 3. Docker Basics
  • 4. Pull the image from Docker Registry
  • 5. Dockerfile
  • 6. Push the Docker Image to ECR
  • 7. Hands On - Amazon ECR for AWS CodeBuild
  • 8. Summary

  • 12. Practical MLOps - Amazon Sagemaker
  • 1. What is AWS Sagemaker
  • 2. Why Sagemaker is the most preferred tool
  • 3. Setting Up the Sagemaker Studio
  • 4. CleanUp Activity

  • 13. Feature Engineering - Feature Store in Sagemaker
  • 1. What is Feature Engineering
  • 2. Data Wrangler Setup
  • 3. Data Quality and Insights Report
  • 4. Univariate Analysis & Bias Report
  • 5. Target Leakage
  • 6. Data Transformation
  • 7. Data Transformation - Custom Script
  • 8. Export to S3
  • 9. Export to Sagemaker Feature Store
  • 10. Create DataFrame using Feature Store
  • 11. Feature Engineering on Sagemaker Notebook Instance
  • 12. Feature Engineering with Sagemaker Processing
  • 13. Summary

  • 14. Training, Tuning & Deploying the Model
  • 1. Training the xgboost
  • 2. Deploy the Model
  • 3. Create End Point and End Point Configuration
  • 4. Automatic Model Tuning

  • 15. Create Custom Models
  • 1. Introduction to Bring own Training Script
  • 2. Use Custom Model created with Tensorflow
  • 3. Use Custom Model created with Pytorch
  • 4. Use Custom Model created with sklearn

  • 16. MLOps Sagemaker Pipelines
  • 1. Sagemaker Pipelines Introduction
  • 2. Sagemaker Training Pipeline
  • 3. Sagemaker Inference Pipeline
  • 4. Advanced MLOps pipeline
  • 5. Architecture Overview
  • 6. System Setup for Cloud9
  • 7. Create Data Repository for MLOps
  • 8. Pipeline Assets Introduction
  • 9. Push ETL Assets to CodeCommit
  • 10. Training and Inference test Asset
  • 11. Run Unit Test on Train & Predict
  • 12. System Test Asets
  • 13. Quick Summary on Assets
  • 14. Working with Pipeline components
  • 15. Create MLOps Pipeline
  • 16. Execution of MLOps Pipeline
  • 17. Invoke Load Simulation test
  • 18. Generate Visualization with Cloud watch logs
  • 19. Data Quality Drift, Baseline, Inference Data
  • 20. CleanUp

  • 17. References (V2)
  • 1. AWS CodeDeploy Introduction
  • 2. AWS CodeDeploy Hands On
  • 3. Appspec.yml Deep Dive
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    تاریخ انتشار: ۲۱ شهریور ۱۴۰۲
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