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
AWS AI ML Stack
- 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
Summary
Standard:
AWS Cert MLS
Lessons:
18+ Lessons
Delivery Method:
Online
Language:
English