AWS Certified Machine Learning Study Guide: Specialty (MLS-C01)

This Course Includes:

Gain the skills required to pass the AWS ML specialty exam with the AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) course and lab. The lab provides a hands-on learning experience of machine learning in a safe, online environment. The purpose of this course is for you to understand the concepts and principles behind ML, with the practical goal of passing the AWS Certified Machine Learning Specialty exam. This course is intended for professionals who perform a data science, machine learning engineer role.

What you will get:

The AWS Certified Machine Learning – Specialty certification validates your understanding of foundational ML concepts, foundations of statistics, data analysis, exploration, feature engineering, and common ML algorithms. In addition to this, this certification focuses on your ability to deploy those solutions on AWS and to be able to architect an end-to-end solution on AWS from data ingestion to model deployment and monitoring using a host of relevant AWS services for a given business use case.

Lessons 1: Introduction

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

Lessons 2: AWS AI ML Stack

  • Amazon Rekognition
  • Amazon Textract
  • Amazon Transcribe
  • Amazon Translate
  • Amazon Polly
  • Amazon Lex
  • Amazon Kendra
  • Amazon Personalise
  • Amazon Forecast
  • Amazon Comprehend
  • Amazon CodeGuru
  • Amazon Augmented AI
  • Amazon SageMaker
  • AWS Machine Learning Devices

Lessons 3: Supporting Services from the AWS Stack

  • Storage
  • Amazon VPC
  • AWS Lambda
  • AWS Step Functions
  • AWS RoboMaker

Lessons 4: Business Understanding

  • Phases of ML Workloads
  • Business Problem Identification

Lessons 5: Framing a Machine Learning Problem

  • ML Problem Framing

Lessons 6: Data Collection

  • Basic Data Concepts
  • Data Repositories
  • Data Migration to AWS

Lessons 7: Data Preparation

  • Data Preparation Tools

Lessons 8: Feature Engineering

  • Feature Engineering Concepts
  • Feature Engineering Tools on AWS

Lessons 9: Model Training

  • Common ML Algorithms
  • Local Training and Testing
  • Remote Training
  • Distributed Training
  • Monitoring Training Jobs
  • Debugging Training Jobs
  • Hyperparameter Optimisation

Lessons 10: Model Evaluation

  • Experiment Management
  • Metrics and Visualisation

Lessons 11: Model Deployment and Inference

  • Deployment for AI Services
  • Deployment for Amazon SageMaker
  • Advanced Deployment Topics

Lessons 12: Application Integration

  • Integration with On-Premises Systems
  • Integration with Cloud Systems
  • Integration with Front-End Systems

Exam Essentials Lessons 13: Operational Excellence Pillar for ML

  • Operational Excellence on AWS

Lessons 14: Security Pillar

  • Security and AWS
  • Secure SageMaker Environments
  • AI Services Security

Lessons 15: Reliability Pillar

  • Reliability on AWS
  • Change Management for ML
  • Failure Management for ML

Lessons 16: Performance Efficiency Pillar for ML

  • Performance Efficiency for ML on AWS

Lessons 17: Cost Optimization Pillar for ML

  • Common Design Principles
  • Cost Optimisation for ML Workloads

Lessons 18: Recent Updates in the AWS AI/ML Stack

  • New Services and Features Related to AI Services
  • New Features Related to Amazon SageMaker

Hands-on LAB Activities


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

Supporting Services from the AWS Stack

  • Creating an AWS Lambda Function
  • Using Step Functions

Data Collection

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

Data Preparation

  • Using Amazon Athena
  • Using AWS Glue

Model Training

  • Performing PCA in SageMaker
  • 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

Model Deployment and Inference

  • Deploying an ML Model using AWS SageMaker

Application Integration

  • Creating an AWS Backup
  • Creating a Model

Operational Excellence Pillar for ML

  • Enabling Versioning in the Amazon S3 Bucket

Security Pillar

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

Reliability Pillar

  • Understanding Production Security
  • Creating an Auto Scaling Group

Performance Efficiency Pillar for ML

  • Creating an Amazon EFS

Recent Updates in the AWS AI/ML Stack

  • Creating an Amazon Redshift Cluster

Exam FAQs

Before you take this exam, it is recommended to have: At least two years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud Ability to express the intuition behind basic ML algorithms Experience performing basic hyperparameter optimization Experience with ML and deep learning frameworks Ability to follow model-training, deployment, and operational best practices

The fee is GBP £300

PSI or Pearson VUE

Multiple choice and multiple response





18+ Lessons

Delivery Method:




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