Practical Mlops For Data Scientists & Devops Engineers - Aws

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

What you'll learn

Configuring the CI/CD Pipeline for Machine Learning Projects

Ability to track the source code & training images, configuration files with Git Based Repository – AWS CodeCommit

Ability to Perform the Build using AWS CodeBuild

Ability to Deploy the Application on Server using AWS CodeDeploy

Orchestrate the MLOps steps using AWS CodePipeline

Identify appropriate AWS services to implement ML solutions

Perform the Load testing

Monitoring the End Point Performance

Monitoring the Model Drift

The ability to follow model-training best practices

The ability to follow deployment best practices

The ability to follow operational best practices

Requirements

Basic knowledge of AWS

Account with AWS for practical Hand-On

Basic knowledge of Machine Learning & Deep Learning

Description

This course - Practical MLOps for Data Scientists & DevOps Engineers with AWS is intended for individuals who wants to perform an artificial intelligence/machine learning (AI/ML) development or data science role as close to Production Level working. This course helps you in improving your ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud with Practices of DevOps for Machine Learning .Right now, you may be aware of basics of Machine learning, but skills expected by employer is – more than what you can run from local notebook.From Employer perspective, its expected that Candidates to have :· The ability to follow model-training best practices on Large Datasets on cloud· The ability to follow deployment best practices so that it will work always· The ability to follow operational best practices so that there will be Zero downtimeIn short, you are expected to solve the Business problem by implementing on the dataset, not just work on the personal laptop.In this learning journey of this course, we will follow the structured learning journey, which takes you in a logical way to understand the topics in a clear and detailed manner with relevant Practical Exercises/Demo.The course structure is as follows:Section 1 : About AWSMLOPS Course and InstructorSection 2 : Introduction to MLOpsSection 3 : DevOps for Data ScientistsSection 4: Getting Started with AWSSection 5: Linux Basics for MLOpsSection 6: Source code Management using GIT - AWS CodeCommitSection 7: Crash Course on YAMLSection 8: AWS CodeBuildSection 9: AWS CodeDeploySection 10: AWS CodePipelineSection 11 : Docker ContainersSection 12 : Practical MLOps - Amazon SagemakerSection 13 : Feature Engineering - Feature Store in SagemakerSection 14: Training, Tuning & Deploying the ModelSection 15 : Create Custom ModelsSection 16 : MLOps Sagemaker PipelinesAll the source code is shared on github, which ensures that- you get to access from anywhere and always have the latest version.Below are the Tools, Technologies and Concepts covered as part of this Course:· Ingestion/Collection· Processing/ETL· Data analysis/visualization· Model training· Model deployment/inference· Operational Aspectes· AWS ML application services· Notebooks and integrated development environments (IDEs)· AWS CodeCommit· Amazon Athena· AWS Batch· Amazon EC2· Amazon Elastic Container Registry (Amazon ECR)· AWS Glue· Amazon SageMaker· Amazon CloudWatch· AWS Lambda· Amazon S3

Overview

Section 1: About AWS MLOps Course and Instructor

Lecture 1 About the MLOps with AWS Course

Lecture 2 How to make the most of this course?

Lecture 3 Source Code of this course

Section 2: Introduction to MLOps

Lecture 4 What & Why MLOps

Lecture 5 Quick Hands On Demo on MLOps

Lecture 6 MLOps Fundamentals

Lecture 7 MLOps Fundamentals - Deep Dive

Lecture 8 Why DevOps alone is not Suitable for Machine Learning ?

Lecture 9 What is AWS & its Benefits

Lecture 10 Technical Stack of AWS for MLOps & Machine Learning

Section 3: DevOps for Data Scientists

Lecture 11 What is SDLC & Why its Important

Lecture 12 Types of SDLC

Lecture 13 Waterfall Vs Agile Vs DevOps

Lecture 14 DevOps Lifecycle & Tools in AWS

Section 4: Getting Started with AWS

Lecture 15 What do we cover in this section ?

Lecture 16 Create AWS Account

Lecture 17 Setting up MFA on Root Account

Lecture 18 Create IAM Account and Account Alias

Lecture 19 Setup CLI with Credentials

Lecture 20 IAM Policy

Lecture 21 IAM Policy generator & attachment

Lecture 22 Delete the IAM User

Lecture 23 S3 Bucket and Storage Classes

Lecture 24 Creation of S3 Bucket from Console

Lecture 25 Creation of S3 Bucket from CLI

Lecture 26 Version Enablement in S3

Lecture 27 Introduction EC2 instances

Lecture 28 Launch EC2 instance & SSH into EC2 Instances

Lecture 29 Clean Up Activity

Section 5: Linux Operating System for DevOps and Data Scientists

Lecture 30 What do we learn in this section ?

Lecture 31 Linux Features & Bash

Lecture 32 How to Launch EC2 Instances (Quick Refresh)

Lecture 33 Linux Basic Commands

Section 6: Source code Management using GIT - CodeCommit

Lecture 34 Introduction to CI CD Pipeline

Lecture 35 Introduction to AWS Code Commit & DVCS

Lecture 36 Git Initial config & Git Commands

Lecture 37 Setting up the workspace for Git

Lecture 38 Git Workflow

Lecture 39 Adding files to Staging Area

Lecture 40 Staged Differences

Lecture 41 Git Unstage

Lecture 42 Git Reset & Revert

Lecture 43 AWS Code Commit Remote Git Commands

Lecture 44 Cloning and Branching

Lecture 45 Git Branching Hands On Part 1

Lecture 46 Git Branching Hands On Part 2

Lecture 47 Git Conflicts & Resolving them

Lecture 48 Git Rebase Vs Git Merge

Lecture 49 Git Stash Introduction

Lecture 50 Git Stash Hands On

Lecture 51 AWS Code Commit Security

Lecture 52 AWS Code Commit Security - Hands On

Lecture 53 AWS Code Commit Integration - Triggers - Notifications - CloudWatch - EventBridg

Lecture 54 Summary

Section 7: YAML Crash Course

Lecture 55 YAML Crash Course

Section 8: AWS CodeBuild

Lecture 56 Introduction to AWS CodeBuild

Lecture 57 Create First CodeBuild Project

Lecture 58 buildspec.yml deep dive

Lecture 59 Code Build Hands On

Lecture 60 Environment Variables in CodeBuild & buildspec.yml deep dive Hands On

Lecture 61 Working CodeBuild Artifacts Hands On

Lecture 62 AWS CodeBuild Triggers

Lecture 63 CleanUp Activity

Section 9: AWS Code Deploy

Lecture 64 AWS CodeDeploy Introduction

Lecture 65 First AWS CodeDeploy - Intro to Hands On

Lecture 66 First AWS CodeDeploy

Lecture 67 appspec.yml - Deep Dive

Lecture 68 CodeDeploy Summary

Section 10: Code Pipeline

Lecture 69 AWS CodePipeline Introduction

Lecture 70 Create CodePepeline - Hands On

Lecture 71 Automatic CI CD Process with Manual Approval

Lecture 72 Summary & CleanUp

Section 11: Docker Containers

Lecture 73 Introduction to Docker

Lecture 74 Installation of Docker Desktop

Lecture 75 Docker Basics

Lecture 76 Pull the image from Docker Registry

Lecture 77 Dockerfile

Lecture 78 Push the Docker Image to ECR

Lecture 79 Hands On - Amazon ECR for AWS CodeBuild

Lecture 80 Summary

Section 12: Practical MLOps - Amazon Sagemaker

Lecture 81 What is AWS Sagemaker ?

Lecture 82 Why Sagemaker is the most preferred tool

Lecture 83 Setting Up the Sagemaker Studio

Lecture 84 CleanUp Activity

Section 13: Feature Engineering - Feature Store in Sagemaker

Lecture 85 What is Feature Engineering

Lecture 86 Data Wrangler Setup

Lecture 87 Data Quality and Insights Report

Lecture 88 Univariate Analysis & Bias Report

Lecture 89 Target Leakage

Lecture 90 Data Transformation

Lecture 91 Data Transformation - Custom Script

Lecture 92 Export to S3

Lecture 93 Export to Sagemaker Feature Store

Lecture 94 Create DataFrame using Feature Store

Lecture 95 Feature Engineering on Sagemaker Notebook Instance

Lecture 96 Feature Engineering with Sagemaker Processing

Lecture 97 Summary

Section 14: Training, Tuning & Deploying the Model

Lecture 98 Training the xgboost

Lecture 99 Deploy the Model

Lecture 100 Create End Point and End Point Configuration

Lecture 101 Automatic Model Tuning

Section 15: Create Custom Models

Lecture 102 Introduction to Bring own Training Script

Lecture 103 Use Custom Model created with Tensorflow

Lecture 104 Use Custom Model created with Pytorch

Lecture 105 Use Custom Model created with sklearn

Section 16: MLOps Sagemaker Pipelines

Lecture 106 Sagemaker Pipelines Introduction

Lecture 107 Sagemaker Training Pipeline

Lecture 108 Sagemaker Inference Pipeline

Lecture 109 Advanced MLOps pipeline

Lecture 110 Architecture Overview

Lecture 111 System Setup for Cloud9

Lecture 112 Create Data Repository for MLOps

Lecture 113 Pipeline Assets Introduction

Lecture 114 Push ETL Assets to CodeCommit

Lecture 115 Training and Inference test Asset

Lecture 116 Run Unit Test on Train & Predict

Lecture 117 System Test Asets

Lecture 118 Quick Summary on Assets

Lecture 119 Working with Pipeline components

Lecture 120 Create MLOps Pipeline

Lecture 121 Execution of MLOps Pipeline

Lecture 122 Invoke Load Simulation test

Lecture 123 Generate Visualization with Cloud watch logs

Lecture 124 Data Quality Drift, Baseline, Inference Data

Lecture 125 CleanUp

Section 17: References (V2)

Lecture 126 AWS CodeDeploy Introduction

Lecture 127 AWS CodeDeploy Hands On

Lecture 128 Appspec.yml Deep Dive

Anyone preparing for Data Science , Machine Learning & Deep Learning Interviews,Anyone interested in learning how Machine Learning is implemented on Large scale data,Anyone interested in AWS cloud-based machine learning and data science,Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud,Anyone looking to learn the best practices to Operationalize the Machine Learning Models