Machine Learning Engineering on AWS Training

Machine Learning Engineering on AWS is an intermediate-level training program designed for professionals who want to build, deploy, scale, and operationalize machine learning solutions on Amazon Web Services. As organizations increasingly adopt cloud-based artificial intelligence solutions, AWS provides a comprehensive ecosystem for managing the entire machine learning lifecycle—from data preparation and model training to deployment and monitoring.

Throughout this hands-on course, participants gain practical experience using Amazon SageMaker AI, Amazon EMR, SageMaker Data Wrangler, SageMaker Pipelines, Model Registry, MLOps, CI/CD, and advanced monitoring services to build production-ready machine learning applications.

Combining theoretical knowledge, guided labs, and real-world projects, this training prepares learners to develop secure, scalable, and enterprise-grade machine learning solutions on AWS.

Participants are recommended to have:

• Familiarity with basic Machine Learning concepts
• Experience with Python programming
• Knowledge of NumPy, Pandas, and Scikit-Learn
• Basic understanding of cloud computing concepts
• Familiarity with AWS services
• Experience with Git or other version control systems (beneficial)

This course is ideal for:

• Machine Learning Engineers
• Data Scientists
• AI Engineers
• DevOps Engineers
• Cloud Engineers
• SysOps Engineers
• Software Developers
• Data Engineers
• Professionals building ML solutions on AWS

By the end of this course, learners will be able to:

• Build machine learning solutions on AWS.
• Train and deploy models using Amazon SageMaker AI.
• Process large-scale datasets with Amazon EMR.
• Perform data preparation and feature engineering.
• Select appropriate modeling approaches.
• Evaluate and optimize model performance.
• Apply Hyperparameter Tuning techniques.
• Implement production-grade deployment strategies.
• Establish MLOps workflows.
• Monitor model performance and data quality.
• Design secure AWS machine learning architectures.

Introduction to Machine Learning on AWS

Machine Learning Fundamentals

• Machine Learning Fundamentals
• Supervised and unsupervised learning
• Machine learning lifecycle
• Translating business problems into ML solutions

Machine Learning on AWS

• AWS machine learning ecosystem
• Amazon SageMaker AI
• AWS ML services overview
• Cloud-native AI solutions

Responsible AI

• Ethical AI principles
• Fairness and bias mitigation
• Explainable AI
• Responsible machine learning practices

Analyzing Machine Learning Challenges

Business Problem Evaluation

• ML use cases
• Defining business objectives
• Success criteria and KPIs

Training Approaches

• Model development strategies
• Training methodologies
• Algorithm selection techniques

Data Processing and Preparation

Data Types and Management

• Structured and unstructured data
• Data collection processes
• Data storage strategies

AWS Storage Solutions

• Amazon S3
• AWS storage services
• Choosing the right storage architecture

Exploratory Data Analysis

• Data exploration techniques
• Data visualization
• Data quality assessment

Data Transformation and Feature Engineering

Data Cleaning

• Handling missing data
• Correcting inaccurate records
• Removing duplicate entries

Feature Engineering

• Feature creation techniques
• Feature selection strategies
• Data transformation methods

AWS Data Processing Services

• Amazon SageMaker Data Wrangler
• Amazon EMR
• SageMaker Processing

Choosing a Modeling Approach

SageMaker Built-In Algorithms

• SageMaker Built-In Algorithms
• Algorithm selection
• Business use cases

Amazon SageMaker Autopilot

• AutoML
• Automated model development
• Intelligent model recommendations

Model Selection Considerations

• Performance evaluation
• Business alignment
• Cost optimization

Training Machine Learning Models

Model Training Concepts

• Training methodologies
• Training infrastructure
• Distributed training approaches

Training with Amazon SageMaker

• SageMaker Training Jobs
• Resource allocation
• Training optimization techniques

Model Evaluation and Optimization

Performance Evaluation

• Evaluation metrics
• Accuracy and error analysis
• Model comparison methodologies

Hyperparameter Optimization

• Hyperparameter Tuning
• Reducing training time
• Automated optimization strategies

Model Deployment Strategies

Deployment Approaches

• Real-time inference
• Batch inference
• Edge deployment

Inference Infrastructure

• Endpoint management
• Containerized inference
• Resource optimization

A/B Testing

• Traffic shifting strategies
• Model comparison
• Canary deployment approaches

Securing AWS Machine Learning Resources

Identity and Access Management

• IAM policies
• Role-based access controls
• Secure ML environments

Network Security

• Network access controls
• VPC integration
• Data protection strategies

CI/CD Security

• Secure deployment pipelines
• Pipeline protection
• Security validation controls

MLOps and Automation

MLOps Fundamentals

• Machine Learning Operations (MLOps)
• Model lifecycle management
• Continuous integration and delivery

Amazon SageMaker Pipelines

• Amazon SageMaker Pipelines
• Workflow automation
• Process standardization

Model Registry

• Amazon SageMaker Model Registry
• Model versioning
• Model governance

Monitoring Model Performance and Data Quality

Model Monitoring

• SageMaker Model Monitor
• Performance tracking
• Production observability

Data Drift and Model Drift

• Detecting data drift
• Monitoring model degradation
• Automated remediation strategies

Automated Troubleshooting and Continuous Improvement

• Anomaly detection
• Automated corrective actions
• Performance optimization
• Operational excellence