[NEW] Ultimate AWS Certified AI Practitioner AIF-C01

Module 1: Introduction & Exam Blueprint

⏳ 20 minutes

• 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

⏳ 1 hour

• 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

⏳ 1 hour

• 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

⏳ 1 hour

• 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

⏳ 45 minutes

• 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

⏳ 45 minutes

• 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

⏳ 45 minutes

• 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

⏳ 30 minutes

• 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

⏳ 30 minutes

• 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 controls.

Module 10: Practice Exam & Exam Strategies

⏳ 30 minutes

• Take multiple practice quizzes aligned to each domain.

• Review detailed explanations and exam-taking tips.

• Plan your final study timeline and exam registration process.

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