AWS Machine Learning Engineer Associate
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Understand AI Fundamentals
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Identify Business Opportunities
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Plan & Execute Projects
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Evaluate Impact & Ethics
60 Hours
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Beginner level
No Prior Knowledge Required
Flexible Learning
Available in Online, Offline, and
Hybrid Modes
60 Hours
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Program Overview
The AWS Machine Learning Associate program equips learners with the knowledge and hands-on skills to design, build, train, and deploy machine learning and generative AI solutions on the AWS Cloud. Covering data engineering, model training, MLOps, and security, the course prepares you for the AWS Certified Machine Learning Associate exam and real-world industry applications.
Key Features
Short-duration / modular format (ranges from 1 day to 30+ hours) for focused learning
Hybrid delivery mode with hands-on labs & projects
Beginner-friendly; minimal prerequisites (basic cybersecurity or networking background useful)
Instruction + exam prep sessions + practice questions
Real-world use cases: threat detection, phishing, malware analysis, anomaly detection, adversarial attacks, etc.
Certificate awarded on completion
Skills Covered
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Generative AI + ML models (GANs, VAEs, LLM basics) in security
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Adversarial attacks & defenses, threat modelling
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Network anomaly & intrusion detection
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Malware & phishing detection and analysis
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Incident response automation & threat intelligence forecasting
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Ethical, legal, and privacy concerns in AI security
Course Curriculum
Cloud & Machine Learning Foundations
Module 1 • 10 hour to complete
Module 1 • 10 hour to complete
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Introduction to AWS Cloud for AI & ML
• Understanding Data Types, Formats, and Properties
• Data Architectures: Data Warehouses, Lakes, Lakehouses, and Mesh
• ETL Pipelines and Orchestration
• Understanding Data Types, Formats, and Properties
• Data Architectures: Data Warehouses, Lakes, Lakehouses, and Mesh
• ETL Pipelines and Orchestration
Data Ingestion & Storage
Module 2 • 10 hour to complete
Module 2 • 10 hour to complete
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Object Storage with Amazon S3 (security, lifecycle, replication, encryption)
• Block and File Storage: Amazon EBS, EFS, and FSx
• Streaming Data with Amazon Kinesis and Amazon MSK (Kafka)
• Handling Common Data Sources and Formats
• Block and File Storage: Amazon EBS, EFS, and FSx
• Streaming Data with Amazon Kinesis and Amazon MSK (Kafka)
• Handling Common Data Sources and Formats
Data Transformation & Preparation
Module 3 • 10 hour to complete
Module 3 • 10 hour to complete
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Big Data Processing with Amazon EMR and Apache Spark
• Data Cleansing & Feature Engineering (scaling, encoding, missing data, outliers)
• Data Integration with AWS Glue and Glue DataBrew
• Querying and Analytics with Amazon Athena
• Data Labeling with SageMaker Ground Truth and Mechanical Turk
• Data Cleansing & Feature Engineering (scaling, encoding, missing data, outliers)
• Data Integration with AWS Glue and Glue DataBrew
• Querying and Analytics with Amazon Athena
• Data Labeling with SageMaker Ground Truth and Mechanical Turk
AWS Managed AI Services
Module 4 • 10 hour to complete
Module 4 • 10 hour to complete
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Language AI: Amazon Comprehend, Translate, Transcribe, Polly
• Vision & Conversational AI: Amazon Rekognition, Amazon Lex
• Business AI Services: Amazon Personalize, Textract, Kendra
•Responsible AI with Augmented AI (A2I) and Guardrails
• Specialized AI Solutions: Fraud Detector, Lookout, Amazon Q (Business, Developer, Apps)
• Vision & Conversational AI: Amazon Rekognition, Amazon Lex
• Business AI Services: Amazon Personalize, Textract, Kendra
•Responsible AI with Augmented AI (A2I) and Guardrails
• Specialized AI Solutions: Fraud Detector, Lookout, Amazon Q (Business, Developer, Apps)
Machine Learning with Amazon SageMaker
Module 5 • 10 hour to complete
Module 5 • 10 hour to complete
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SageMaker Ecosystem Overview
• Data Processing, Training, and Deployment
• Model Explainability & Monitoring (Clarify, Model Monitor, Feature Store)
• Built-in Algorithms: Linear Learner, XGBoost, K-Means, PCA, Random Cut Forest, BlazingText, Object Detection, etc.
• Labs with SageMaker Studio, Canvas, and Data Wrangler
• Data Processing, Training, and Deployment
• Model Explainability & Monitoring (Clarify, Model Monitor, Feature Store)
• Built-in Algorithms: Linear Learner, XGBoost, K-Means, PCA, Random Cut Forest, BlazingText, Object Detection, etc.
• Labs with SageMaker Studio, Canvas, and Data Wrangler
Model Training, Evaluation & Optimization
Module 6 • 10 hour to complete
Module 6 • 10 hour to complete
•Deep Learning Foundations: CNNs, RNNs, Transformers
• Neural Network Tuning & Regularization
• Evaluation Metrics: Precision, Recall, AUC, RMSE, R²
• Hyperparameter Tuning (AMT, AutoML, Autopilot
• Distributed Training at Scale (Data Parallelism, Model Parallelism)
• Neural Network Tuning & Regularization
• Evaluation Metrics: Precision, Recall, AUC, RMSE, R²
• Hyperparameter Tuning (AMT, AutoML, Autopilot
• Distributed Training at Scale (Data Parallelism, Model Parallelism)
Generative AI Fundamentals
Module 7 • 10 hour to complete
Module 7 • 10 hour to complete
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Transformer Architecture and Self-Attention
• LLM Concepts (tokens, embeddings, fine-tuning, transfer learning)
• AWS Foundation Models & JumpStart with Hugging Face
• Practical Labs: Tokenization, GPT, and Model Explainability
• LLM Concepts (tokens, embeddings, fine-tuning, transfer learning)
• AWS Foundation Models & JumpStart with Hugging Face
• Practical Labs: Tokenization, GPT, and Model Explainability
Building GenAI Applications with Amazon Bedrock
Module 8 • 10 hour to complete
Module 8 • 10 hour to complete
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Working with Foundation Models in Bedrock Playground
• Retrieval-Augmented Generation (RAG) with Knowledge Bases and Vector Stores
• Guardrails for Responsible AI
• Building Agents with Bedrock (action groups, orchestration, integration)
• Continuous Model Improvement and Evaluation
• Retrieval-Augmented Generation (RAG) with Knowledge Bases and Vector Stores
• Guardrails for Responsible AI
• Building Agents with Bedrock (action groups, orchestration, integration)
• Continuous Model Improvement and Evaluation
MLOps on AWS
Module 9 • 10 hour to complete
Module 9 • 10 hour to complete
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Deployment Guardrails and Shadow Testing
• SageMaker Deployment Options (batch, real-time, serverless, edge)
• CI/CD with CodePipeline, CodeBuild, and CodeDeploy
• Containerization & Orchestration: Docker, ECS, EKS, AWS Batch
• Workflow Automation: Step Functions, EventBridge, Airflow
• Data Governance with AWS Lake Formation
• SageMaker Deployment Options (batch, real-time, serverless, edge)
• CI/CD with CodePipeline, CodeBuild, and CodeDeploy
• Containerization & Orchestration: Docker, ECS, EKS, AWS Batch
• Workflow Automation: Step Functions, EventBridge, Airflow
• Data Governance with AWS Lake Formation
Security, Identity & Compliance
Module 10 • 10 hour to complete
Module 10 • 10 hour to complete
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Principles of Least Privilege & Responsible Data Handling
• IAM: Users, Policies, Roles, MFA
• Encryption & Key Management: AWS KMS, Secrets Manager, Macie
• Network Security: VPCs, Gateways, Endpoints, PrivateLink
• Application Security: WAF, Shield
• IAM: Users, Policies, Roles, MFA
• Encryption & Key Management: AWS KMS, Secrets Manager, Macie
• Network Security: VPCs, Gateways, Endpoints, PrivateLink
• Application Security: WAF, Shield
Monitoring, Governance & Cost Optimization
Module 11 • 10 hour to complete
Module 11 • 10 hour to complete
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Observability with CloudWatch, Logs, Metrics, Alarms, X-Ray
• Governance with AWS Config & CloudTrail
• Cost Management: AWS Budgets, Cost Explorer, Trusted Advisor
• Visualization and Reporting with Amazon QuickSight
• Governance with AWS Config & CloudTrail
• Cost Management: AWS Budgets, Cost Explorer, Trusted Advisor
• Visualization and Reporting with Amazon QuickSight
Machine Learning Best Practices
Module 12 • 10 hour to complete
Module 12 • 10 hour to complete
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Designing ML Systems with Responsible AI Principles
• ML Lifecycle and Design Patterns
• Business Problem Framing and Goal Alignment
• Deployment and Monitoring for Business Outcomes
• AWS Well-Architected ML Lens
• ML Lifecycle and Design Patterns
• Business Problem Framing and Goal Alignment
• Deployment and Monitoring for Business Outcomes
• AWS Well-Architected ML Lens
Certification Preparation
Module 13 • 10 hour to complete
Module 13 • 10 hour to complete
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Practice Exam (20 Questions)
• Exam Guide Walkthrough & New Question Types
• Preparation Resources and Study Tips
• AWS Certification Paths & Next Steps
• Exam Guide Walkthrough & New Question Types
• Preparation Resources and Study Tips
• AWS Certification Paths & Next Steps
Wrap-Up & Career Pathways
Module 14 • 10 hour to complete
Module 14 • 10 hour to complete
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Final Review & Key Takeaways
• Industry Applications of AI & ML on AWS
• Continuing Your AWS Learning Journey
• Industry Applications of AI & ML on AWS
• Continuing Your AWS Learning Journey
Tools & Technologies
Completion Certificate
Job Role
- AI Security Analyst
- Cybersecurity Engineer
- Incident Response Engineer
- Threat Intelligence Specialist
- Security Operations Center (SOC) Analyst / Engineer
- Network Security Engineer
- Penetration Tester (AI & ML assisted)
- Secure DevOps / MLOps Engineer
Training Option
OFFLINE
₹7777
- 24/7 support
- 24/7 support
- 24/7 support
ONLINE
₹5555
- 24/7 support
- 24/7 support
- 24/7 support
HYBRID
₹6666
- 24/7 support
- 24/7 support
- 24/7 support
Why Join this Program
Earn a job
Receive complete job assistance tailored to your career goals. Get expert placement guidance to confidently step into the industry.
Leverage knowledge from industry experts
Learn directly from seasoned Trainers and Gain real-world insights that go beyond textbooks.
Industry-relevant Tools & Practical Learning
Get hands-on experience with the latest tools used by top companies. Hands-on learning through 200+ exercises and 10+ projects with seamless access to integrated labs.
Structured, industry-vetted curriculum
A curriculum shaped by experts to meet evolving industry demands. Structured learning ensures you're career-ready from day one.
Integrated with Gen AI Modules
The curriculum includes cutting-edge Generative AI modules designed to align with emerging tech trends.
Interview preparation & Placement assistance
Sharpen your interview skills with practical training and expert guidance. Receive complete placement support to connect with top recruiters.
FAQ
Basic Python is enough; we cover everything from scratch.
No — this course teaches ML fundamentals too.
Absolutely — this is designed for career transition.
You’ll need to put in extra effort, but many succeed.
Yes — the curriculum includes real-world LLM integration.
Yes — including techniques used by AI professionals.