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CSC430: Machine Learning Principles

(WU-CSC430.AE1)
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Skills You’ll Get

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1

Introduction

  • The AWS Certified Machine Learning Specialty Exam
  • Study Guide Features
  • AWS Certified Machine Learning Specialty Exam Objectives
2

AWS AI ML Stack

  • Amazon Rekognition
  • Amazon Textract
  • Amazon Transcribe
  • Amazon Translate
  • Amazon Polly
  • Amazon Lex
  • Amazon Kendra
  • Amazon Personalize
  • Amazon Forecast
  • Amazon Comprehend
  • Amazon CodeGuru
  • Amazon Augmented AI
  • Amazon SageMaker
  • AWS Machine Learning Devices
  • Summary
  • Exam Essentials
3

Supporting Services from the AWS Stack

  • Storage
  • Amazon VPC
  • AWS Lambda
  • AWS Step Functions
  • AWS RoboMaker
  • Summary
  • Exam Essentials
4

Business Understanding

  • Phases of ML Workloads
  • Business Problem Identification
  • Summary
  • Exam Essentials
5

Framing a Machine Learning Problem

  • ML Problem Framing
  • Recommended Practices
  • Summary
  • Exam Essentials
6

Data Collection

  • Basic Data Concepts
  • Data Repositories
  • Data Migration to AWS
  • Summary
  • Exam Essentials
7

Data Preparation

  • Data Preparation Tools
  • Summary
  • Exam Essentials
8

Feature Engineering

  • Feature Engineering Concepts
  • Feature Engineering Tools on AWS
  • Summary
  • Exam Essentials
9

Model Training

  • Common ML Algorithms
  • Local Training and Testing
  • Remote Training
  • Distributed Training
  • Monitoring Training Jobs
  • Debugging Training Jobs
  • Hyperparameter Optimization
  • Summary
  • Exam Essentials
10

Model Evaluation

  • Experiment Management
  • Metrics and Visualization
  • Summary
  • Exam Essentials
11

Model Deployment and Inference

  • Deployment for AI Services
  • Deployment for Amazon SageMaker
  • Advanced Deployment Topics
  • Summary
  • Exam Essentials
12

Application Integration

  • Integration with On-Premises Systems
  • Integration with Cloud Systems
  • Integration with Front-End Systems
  • Summary
  • Exam Essentials
13

Operational Excellence Pillar for ML

  • Operational Excellence on AWS
  • Summary
  • Exam Essentials
14

Security Pillar

  • Security and AWS
  • Secure SageMaker Environments
  • AI Services Security
  • Summary
  • Exam Essentials
15

Reliability Pillar

  • Reliability on AWS
  • Change Management for ML
  • Failure Management for ML
  • Summary
  • Exam Essentials
16

Performance Efficiency Pillar for ML

  • Performance Efficiency for ML on AWS
  • Summary
  • Exam Essentials
17

Cost Optimization Pillar for ML

  • Common Design Principles
  • Cost Optimization for ML Workloads
  • Summary
  • Exam Essentials
18

Recent Updates in the AWS AI/ML Stack

  • New Services and Features Related to AI Services
  • New Features Related to Amazon SageMaker
  • Summary
  • Exam Essentials

1

AWS AI ML Stack

  • Detecting Objects in an Image
  • Using Amazon Translate
  • Using Amazon Transcribe and Polly
  • Using Amazon SageMaker
2

Supporting Services from the AWS Stack

  • Creating an AWS Lambda Function
  • Using Step Functions
3

Data Collection

  • Creating an Amazon DynamoDB Table
  • Creating a Kinesis Firehose Delivery Stream
4

Data Preparation

  • Using Amazon Athena
  • Using AWS Glue
5

Model Training

  • Performing the K-Means Clustering
  • Creating Amazon EventBridge Rules that React to Events
  • Creating a CloudWatch Dashboard and Adding a Metric to it
  • Creating CloudTrail
6

Model Deployment and Inference

  • Deploying an ML Model Using AWS SageMaker
7

Application Integration

  • Creating an AWS Backup
  • Creating a Model
8

Operational Excellence Pillar for ML

  • Enabling Versioning in the Amazon S3 Bucket
9

Security Pillar

  • Using Amazon EC2
  • Configuring a Key
  • Using Amazon SageMaker Notebook Instance
  • Attaching an AWS IAM Role to an Instance
10

Reliability Pillar

  • Understanding Production Security
  • Creating an Auto Scaling Group
11

Performance Efficiency Pillar for ML

  • Creating an Amazon EFS
12

Recent Updates in the AWS AI/ML Stack

  • Creating an Amazon Redshift Cluster

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