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

Learn, prepare and practice for the AWS exam. Gain real-world experience with hands-on Labs and case studies.

(MLS-C01.AE1) / ISBN : 978-1-64459-387-5
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Gamified TestPrep
Hands-On Labs
AI Tutor (Add-on)
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About This Course

AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) is a preparatory course that provides a structured and detailed learning approach to pass the certification exam. The course focuses on foundational ML concepts, foundations of statistics, data analysis, exploration, feature engineering, and common ML algorithms. In addition to this, it equips you with the skills 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. This AWS Machine Learning course comes with an exam-focused approach curriculum that is totally aligned with the latest exam objectives to help you prepare and pass the exam easily. 

Skills You’ll Get

  • Expertise in using AWS AI/ML services like Amazon SageMaker, Amazon Rekognition, and more
  • Understanding of ML algorithms like linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, and deep learning models
  • Data Science pipelines, the entire ML lifecycle 
  • Awareness of AWS infrastructure services like Amazon S3, Amazon EC2, Amazon RDS, Amazon VPC, and AWS Lambda 
  • Design and implement scalable and cost-effective cloud architectures for ML applications
  • Skilled with deep learning frameworks like TensorFlow and PyTorch, and their application to tasks like image recognition, natural language processing, and time series analysis
  • Understanding of reinforcement learning concepts and algorithms 
  • Tuning hyperparameter to optimize model performance
  • Knowledge of ML deployment models as web services, containerized applications, or serverless functions
  • Utilizing MLOps for managing the entire ML lifecycle, including version control, continuous integration/continuous delivery (CI/CD), and monitoring

Download Course Outline

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

Any questions?
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Know more about the AWS Machine Learning Study Guide: Specialty (MLS-C01) course & certification.

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MLS-C01 is a certification exam conducted by AWS. It validates your skills for designing, building, training, and deploying machine learning (ML) models on the AWS platform. It is suitable for all those with some ML experience wanting to demonstrate their expertise in using AWS services.

There are no formal prerequisites for the AWS Certified Machine Learning Specialty certification. However it’s an advanced-level course that requires a strong foundation in ML Learning concepts. To boost your understanding and speed up the learning process, you can do some base-level courses like AWS Certified Cloud Practitioner & AWS Certified Developer - Associate.

Yes, it covers the latest updates in ML learning concepts and provides a comprehensive coverage on several advanced topics. Some of the advanced ML topics include Deep Learning, Reinforcement Learning, Generative Models, Transfer Learning, Time Series Analysis, and Computer Vision.

The exam has 50 questions in total. It has multiple choice and multiple response questions.

The certification exam price is $300.

This AWS certification offers many benefits like career advancement, technical proficiency, credibility, and a competitive advantage, making it a valuable asset for you. If you want to advance your careers in machine learning and cloud computing this can be the ideal starting point for you.

The expected annual salary range of an AWS certified professional is anywhere between 136,500 USD to 198,500 USD.

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