Practical MLOps for Data Scientists & DevOps Engineers – AWS

سرفصل های دوره

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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تولید کننده: Udemy