[NEW] Ultimate AWS Certified AI Practitioner AIF-C01 Course Syllabus

Overview: This course is designed to prepare beginners for the AWS Certified AI Practitioner (AIF-C01) exam and real-world AI implementation on AWS. It covers core AI/ML services, hands-on model development, pre-built APIs, generative AI integration, and best practices in security and cost management. With approximately 6 hours of on-demand video content, learners will progress through structured modules combining theory, labs, and practical implementation across AWS's AI ecosystem.

Module 1: Introduction & Exam Blueprint

Estimated time: 0.3 hours

• Review the AI/ML Specialty exam domains and weighting
• Set up your AWS account, IAM roles, and SageMaker environment
• Understand the AWS shared responsibility model for AI workloads

Module 2: ML Fundamentals on AWS

Estimated time: 1 hours

• Differentiate between regression, classification, clustering, and recommendation tasks
• Explore common algorithms: XGBoost, linear learner, and K-means in SageMaker
• Understand overfitting, underfitting, and cross-validation strategies

Module 3: Data Preparation & Feature Engineering

Estimated time: 1 hours

• Ingest data from S3, Redshift, and AWS Glue Data Catalog
• Clean, transform, and visualize datasets using SageMaker Data Wrangler
• Generate features and apply normalization, encoding, and dimensionality reduction

Module 4: Model Training & Tuning

Estimated time: 1 hours

• Launch training jobs with built-in algorithms and custom containers
• Use SageMaker Automatic Model Tuning (Hyperparameter Optimization)
• Track experiments and compare model metrics with SageMaker Experiments

Module 5: Model Deployment & Monitoring

Estimated time: 0.8 hours

• Deploy real-time endpoints and batch transform jobs
• Monitor inference latency, error rates, and invoke autoscaling policies
• Implement A/B testing and Canary deployments for model updates

Module 6: Computer Vision with Rekognition

Estimated time: 0.8 hours

• Use Rekognition APIs for object detection, facial analysis, and content moderation
• Create custom labels projects using Rekognition Custom Labels
• Integrate with S3 and Lambda for event-driven image processing

Module 7: Natural Language Processing

Estimated time: 0.8 hours

• Analyze text with Comprehend for sentiment, entities, and key phrases
• Build conversational agents using Amazon Lex and integrate with AWS Lambda
• Translate and transcribe audio using Amazon Translate and Transcribe services

Module 8: Generative AI & LLM Integration

Estimated time: 0.5 hours

• Explore Amazon Bedrock and foundation models for text generation
• Use Amazon CodeWhisperer for AI-assisted coding
• Understand cost considerations and best practices for LLM inference

Module 9: Security, Governance & Cost Optimization

Estimated time: 0.5 hours

• Implement IAM policies, KMS encryption, and VPC endpoints for secure ML
• Tag resources, set budgets, and use Cost Explorer to track AI/ML spend
• Apply SageMaker Studio governance with user profiles and domain configurations

Prerequisites

• Familiarity with basic AWS services (e.g., S3, IAM, Lambda)
• Basic understanding of cloud computing concepts
• Access to an AWS account for hands-on labs

What You'll Be Able to Do After

• Pass the AWS Certified AI Practitioner (AIF-C01) exam with confidence
• Build, train, and deploy machine learning models using AWS SageMaker
• Implement pre-built AI services for computer vision and natural language processing
• Integrate generative AI and large language models into applications using Amazon Bedrock
• Apply security, monitoring, and cost optimization best practices to AI workloads