CLOUD-AI.AW1

Cloud Native AI and Machine Learning on AWS

Learn the specifics. Get your hands dirty. This AWI AI machine learning course makes upskilling feel like a chart-topping hit.

13 Interactive Lessons and 121 topics mapped to the official exam objectives

Intermediate Self-paced · 1 year access

13Interactive Lessons

121Topics

12Videos

58Flashcards

58Glossary of terms

01 / Skills you'll get

What you will be able to do

Try Free → No credit card required

Ready to master AWS AI services? This cloud native AWS AI and ML course gives you hands-on experience.

Dive into real-world projects using Amazon SageMaker, Comprehend, Rekognition, and AutoML. Learn feature engineering and neural networks. Then, deploy models with SageMaker endpoints and serverless inference.

ML Models: Master end-to-end pipelines using Amazon SageMaker, from data prep to production-ready deployments.
AI Workflows: Leverage AutoML (Canvas, Autopilot) and MLOps to streamline model training, tuning, and monitoring.
Engineer Smart Features: Transform raw data into powerful inputs with feature engineering for vision, NLP, and tabular datasets.
AWS AI Services: Integrate pre-trained models like Rekognition (CV), Comprehend (NLP), and Lookout (anomaly detection) into real-world apps.
Optimize Performance: Boost models with neural networks, distributed training, and elastic inference for cost-effective scaling.
Data Lakes: Design AWS-based data lakes for ML, ensuring security, reusability, and seamless hydration.

Course Highlights

13 Structured Lessons Comprehensive coverage of core course objectives
1 Year Full Access Self-paced learning accessible anytime on all devices

02 / Lessons & labs

See exactly what you will learn and practice

Download outline (PDF)

Lessons

13 Interactive Lessons · 121 topics

01 Preface 1 topics

Lesson Overview

02 Introducing the ML Workflow 8 topics

Introduction
Evolution of AI and ML
Approaching an ML problem
Overview of the ML workflow
Introducing AI and ML on AWS
Navigating the ML workflow
Conclusion
Points to Remember

03 Hydrating the Data Lake 14 topics

Introduction
Lesson Scenario
The Data Lake
Securing your Buckets
Securing your Data Lake
Data Lakes for Machine Learning
The Importance of Hydration
Setting Up Your AWS Account
Starting Datasets
Streaming Data and the Data Lake
Uncovering Patterns
Amazon Athena
Conclusion
Points to Remember

04 Predicting the Future With Features 25 topics

Introduction
Technical Requirements
Introducing feature engineering
Tokenize and remove punctuations
Feature engineering for computer vision
Resizing Images
Cropping and tiling images
Rotating images
Converting to grayscale
Converting to RecordIO format
Dimensionality reduction with Principal Component Analysis
Feature engineering for tabular datasets
Exploring the data
Imputing missing values
Feature selection
Feature frequency encoding
Target mean encoding
One hot encoding
Feature scaling
Feature normalization
Binning
Feature correlation
Principal Component Analysis
Conclusion
Points to Remember

05 Orchestrating the Data Continuum 6 topics

Introduction
Demystifying the data continuum
Running feature engineering with AWS Glue ETL
Data profiling with AWS Glue DataBrew
Conclusion
Points to Remember

06 Casting a Deeper Net (Algorithms and Neural Networks) 6 topics

Introduction
Introducing Algorithms and Neural networks
Simplifying the Algorithm versus Neural network conundrum
Building ML solutions with Algorithms and Neural Networks
Conclusion
Points to Remember

07 Iteration Makes Intelligence (Model Training and Tuning) 17 topics

Introduction
The Meaning of Training
What Training Means for Deep Learning
GPU vs CPU
AWS Trainium
Transfer Learning
The Mise en Place of Model Training
Defining Model Training and Evaluation Metrics
Setting Up Model Hyperparameters
Script vs Container
Training Data Storage and Compute
Training Scenarios
Linear Regression
Natural Language Processing
Image Classification
Conclusion
Points to Remember

08 Let George Take Over (AutoML in Action) 6 topics

Introduction
Running AutoML with SageMaker Canvas
Automated Hyperparameter Tuning
Using AutoGluon for AutoML
Conclusion
Points to Remember

09 Blue or Green (Model Deployment Strategies) 10 topics

Introduction
Inference Options
Choosing your Compute
Amazon SageMaker Endpoint
Inference at the Edge
Deployment Mechanics
After the Deployment
Updating a Deployed Model
Conclusion
Points to Remember

10 Wisdom at Scale with Elastic Inference 10 topics

Introduction
Understanding SageMaker ML Inference options
SageMaker endpoints for serverless inference
SageMaker transformer for batch inference
Running Inference with SageMaker Hosting
Inference with real-time endpoints
Inference with serverless endpoints
Inference with Batch Transform
Conclusion
Points to Remember

11 Adding Intelligence with Sensory Cognition 5 topics

Introduction
Introducing AWS AI services
Adding sensory cognition to your applications
Conclusion
Points to Remember

12 AI for Industrial Automation 6 topics

Introduction
Overview of AI for Industrial Automation
Cost of Poor Quality or COPQ
Predictive Analytics with Amazon Lookout for Equipment
Conclusion
Points to Remember

13 Operationalized Model Assembly (MLOps and Best Practices) 7 topics

Introduction
Lesson Scenario
MLOps Defined
Orchestration Options
Phase Discrimination
Best Practices using the AWS Well-Architected Lens for Machine Learning
Conclusion

03 / FAQs

Questions before you start

Which AWS service is used for machine learning and AI?

AWS offers a suite of AI/ML services, including:

Amazon SageMaker: End-to-end platform for building, training, and deploying ML models.
AWS AI Services: Pre-trained models like Rekognition (CV), Comprehend (NLP), and Lex (chatbots) for ready-to-use AI solutions.
Amazon Bedrock: For generative AI applications using foundation models (e.g., Meta, Mistral AI).
AWS Trainium/Inferentia: Specialized infrastructure for cost-efficient ML training/inference.

Which certificate is best for AI and machine learning?

Here are the best AWS certifications you can aim for:

AWS Certified Machine Learning – Specialty: Best for hands-on ML engineers validating skills in model building, tuning, and deployment on AWS.
AWS Certified AI Practitioner: Foundational for non-technical roles (e.g., business analysts) to understand AI/ML concepts and AWS services.
AWS Certified Data Engineer – Associate: Complements ML workflows with data pipeline expertise.

Does AWS have an AI certification?

Yes. AWS offers:

AWS Certified AI Practitioner (AIF-C01): Covers AI/ML fundamentals, generative AI, and AWS services like Bedrock and SageMaker. No technical prerequisites.
AWS Certified Machine Learning – Specialty: Advanced certification for ML engineers.

What is the highest-paying AWS certification?

As of 2025, global average salaries for top AWS certs are:

AWS Certified Machine Learning – Specialty: $171,725
AWS Certified Advanced Networking – Specialty: $151,061
AWS Certified Security – Specialty: $158,594

Master Cloud Native AWS AI and ML

Learn, build, deploy, and cash in on AWS AI and ML services.

1 year of full access
Certificate of completion

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