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 |
| Udemy | AWS Certified Machine Learning Course |
AWS Certified Machine Learning Practice Test
| Whizlabs Exam Questions | AWS Machine Learning [145 Practice Tests & 3 Labs] |
| Udemy Practice Test | AWS Certified ML Specialty 75 Practice Questions |
AWS Certified Machine Learning Preparation
| Coursera | Getting 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.
Data Engineering – 20%
Create data repositories for machine learning
Using Amazon S3 as a data repository
Using Amazon Redshift as a data source
Using Amazon RDS Database as an Amazon ML Datasource
Identify and implement a data-ingestion solution
Understand how data is ingested with Amazon SageMaker and a Data Lake on AWS
Case Study: How Kinect Energy ingests data to forecast energy prices
Identify and implement a data-transformation solution
Data transformations in Amazon Machine Learning:
2. Orthogonal Sparse Bigram (OSB) Transformation
4. Remove Punctuation Transformation
5. Quantile Binning Transformation
6. Normalization Transformation
7. Cartesian Product Transformation
Amazon SageMaker Batch Transform
Example: Transform dataset from numpy.array format to the CSV format
Data Rearrangement: Create datasource based on a section of the input data
Exploratory Data Analysis – 24%
Sanitize and prepare data for modeling
Prepare your data in Amazon Machine Learning
Use Amazon SageMaker Ground Truth for Data Labeling
Prepare data in Amazon SageMaker
Perform feature engineering
Understanding the Importance of Feature Transformation
Feature Processing in Amazon Machine Learning
Feature Processing using Spark & Scikit-learn in SageMaker
Analyze and visualize data for machine learning
Analyzing data for Machine Learning
1. Analyzing Data with Amazon Machine Learning
2. Explore, Analyze & Process data
Visualizing data for Machine Learning
1. Visualizing the distribution of data
2. Visualizing insights for binary models
3. Visualizing insights for Regression models
4. Visualizing insights for Multi-class Models
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)
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
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:
Understand the Cross-validation technique for evaluating ML Models
Evaluating Model Fit: Underfitting vs. Overfitting
Evaluate Model Accuracy of the following:
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
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
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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