AWS Certified Machine Learning Specialty Exam Study Guide [MLS-C01]

AWS Certified Machine Learning Specialty Exam Preparation Study Guide CLF-C01

Preparing for the AWS Certified Machine Learning Specialty exam? Don't know where to start? This post is the AWS Certified Machine Learning Certificate Study Guide (with links to each objective in the exam domain).

I have curated a detailed list of articles from AWS documentation and other blogs for each objective of the AWS Certified Machine Learning (MLS-C01) exam. Please share the post within your circles so it helps them to prepare for the exam.

AWS Certified Machine Learning Online Course

Pluralsight (Free Trial)AWS Certified ML Specialty Learning Path [MLS-C01]
LinkedIn Learning [Free Trial]Amazon Web Services Machine Learning Essentials
UdemyAWS Certified Machine Learning Course

AWS Certified Machine Learning Practice Test

Whizlabs Exam QuestionsAWS Machine Learning [145 Practice Tests & 3 Labs]
Udemy Practice TestAWS Certified ML Specialty 75 Practice Questions

AWS Certified Machine Learning Preparation

CourseraGetting Started with AWS Machine Learning
Amazon e-book (PDF)Mastering Machine Learning on AWS

To view other AWS certificate study guides, click here.

Full Disclosure: Some of the links in this post are affiliate links. I receive a commission when you purchase through them.

AWS Certified Machine Learning - Specialty
aws certified machine learning

Amazon link (affiliate)

Modeling – 36%

Frame business problems as machine learning problems

A news article on Applying Machine Learning To Business Problems by Carlos Escapa

Slides from the same session by Carlos

Other interesting articles/experiences to read:

1. Resources from AWS: Formulating the Problem

2. Resources from Amazon: Solving Business Problems with Amazon ML

3. This course from Google is great for understanding the ML Problem framing

4. A Step-by-Step Guide to Machine Learning Problem Framing from Medium

5. Frame a problem as a machine learning problem from Data Science Central

Framing Business Problems for Automatic Machine Learning

 
Select the appropriate model(s) for a given machine learning problem

Amazon Machine Learning: Types of ML Models

Excellent article on how to select an ML algorithm (although for Azure ML)

Review all the built-in algorithms in SageMaker to understand their applicability:

Supervised Learning Algorithms:

DeepAR forecasting (Forecasts time series using Neural Networks)

Factorization Machines (Captures interactions between features)

Image Classification Algorithm (Multi-label image classification)

K-Nearest Neighbors Algorithm (Predicts similar items by evaluating distance)

Linear Learner Algorithm (for Classification or Regression problems)

Object2Vec Algorithm (General-purpose algorithm)

Object Detection Algorithm (Classifies objects in images)

Sequence-to-Sequence Algorithm (for text summarization)

Unsupervised Learning Algorithms:

IP Insights Algorithm (Learns the usage patterns of IPv4 addresses)

K-Means (Finds discrete groups within data)

Latent Dirichlet Allocation (LDA) Algorithm (Unsupervised learning algorithm)

Neural Topic Model (NTM) Algorithm (organize a corpus of documents into topics)

Principal Component Analysis (PCA) Algorithm (for reducing the dimensionality)

Random Cut Forest (RCF) Algorithm (Detects anomalous data points in a data set)

Other Machine Learning Algorithms:

BlazingText Algorithm (Text Classification)

Semantic Segmentation Algorithm (tags every pixel in an image with a label)

XGBoost Algorithm

 
Train machine learning models

Build, Train, and Deploy a Machine Learning Model with SageMaker

Train a Model with Amazon SageMaker

Incremental training of model in SageMaker

Training with Amazon EC2 Spot Instances

Other AWS documentation items to review:

Understanding the training process

Train a Deep Learning model

 
Perform hyperparameter optimization

Understanding the Training Parameters

Hyperparameters available in Amazon ML

How does Hyperparameter Tuning work?

Defining Hyperparameter Ranges

Example of a Hyperparameter Tuning Job

Best Practices for Hyperparameter Tuning

 
Evaluate machine learning models

Evaluate ML Models:

1. Binary Model Insights

2. Multiclass Model Insights

3. Regression Model Insights

Understand the Cross-validation technique for evaluating ML Models

Evaluating Model Fit: Underfitting vs. Overfitting

Evaluate Model Accuracy of the following:

1. Binary Classification

2. Multiclass Classification

3. Regression Models

Machine Learning Implementation and Operations – 20%

Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance

Performance:

1. Review the ML Model's Predictive Performance

Availability:

1. Deploy Multiple Instances Across Availability Zones

Scalability:

1. Amazon SageMaker: Infinitely Scalable Machine Learning Algorithms

2. Review this Whitepaper: Power Machine Learning at Scale

3. Scaling Machine Learning from 0 to millions of users

Resiliency:

1. Resiliency in Amazon SageMaker

2. AWS' approach to operational resilience

 
Recommend and implement the appropriate machine learning services and features for a given problem

All Machine Learning solutions on AWS

Details of each ML/AI services in AWS

 
Apply basic AWS security practices to machine learning solutions

Review the whitepaper on AWS Security processes

Protect Data at Rest

Protect Data in Transit

Secure access to Amazon SageMaker with IAM Roles

Monitoring:

1. Monitor Amazon SageMaker with Amazon CloudWatch

2. Record user actions/interactions with SageMaker with AWS CloudTrail

Let Amazon SageMaker securely connect to resources in VPC

 
Deploy and operationalize machine learning solutions

2 ways to deploy your model:

a. Amazon SageMaker hosting services (to set up an endpoint to get predictions)

Deploy a Model on Amazon SageMaker Hosting Services

Example: Deploying a Model to Amazon SageMaker Hosting Services

b. Amazon SageMaker batch transform (to get predictions on the entire dataset)

Overview: Deploying a model with Amazon SageMaker batch transform

Deploy the Model with Batch Transform

Review deployment best practices

Troubleshoot Amazon SageMaker Model Deployments

This brings us to the end of the AWS Certified Machine Learning – Specialty [MLS-C01] Exam Preparation Study Guide

What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!

In case you are looking for other AWS certificate exams study guides, check out this page

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