uCertify

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.

(CLOUD-AI.AW1) / ISBN : 978-1-64459-743-9
Lessons
AI Tutor (Add-on)

About This Course

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. 

Skills You’ll Get

  • 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.
Download Course Outline

1

Preface

2

Introducing the ML Workflow

  • 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
3

Hydrating the Data Lake

  • 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
4

Predicting the Future With Features

  • 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
5

Orchestrating the Data Continuum

  • Introduction
  • Demystifying the data continuum
  • Running feature engineering with AWS Glue ETL
  • Data profiling with AWS Glue DataBrew
  • Conclusion
  • Points to Remember
6

Casting a Deeper Net (Algorithms and Neural Networks)

  • 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
7

Iteration Makes Intelligence (Model Training and Tuning)

  • 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
8

Let George Take Over (AutoML in Action)

  • Introduction
  • Running AutoML with SageMaker Canvas
  • Automated Hyperparameter Tuning
  • Using AutoGluon for AutoML
  • Conclusion
  • Points to Remember
9

Blue or Green (Model Deployment Strategies)

  • 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

  • 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
  • Adding a SageMaker Elastic Inference (EI) accelerator
  • Conclusion
  • Points to Remember
11

Adding Intelligence with Sensory Cognition

  • Introduction
  • Introducing AWS AI services
  • Adding sensory cognition to your applications
  • Conclusion
  • Points to Remember
12

AI for Industrial Automation

  • Introduction
  • Overview of AI for Industrial Automation
  • Cost of Poor Quality or COPQ
  • Quality Control with Amazon Lookout for Vision
  • Predictive Analytics with Amazon Lookout for Equipment
  • Conclusion
  • Points to Remember
13

Operationalized Model Assembly (MLOps and Best Practices)

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

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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.

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.

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.

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

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