Table of contents
Everything You Need to Know about the AWS Certified AI Practitioner Exam
5m 41s
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Everything You Need to Know about the AWS Certified AI Practitioner Exam | 5m 41s
Introducing Basic AI and ML Concepts
37m 15s
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Introducing Basic AI and ML Concepts | 8m 3s
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How Do Machines Learn? | 10m 52s
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Different Ways Machines Learn | 9m 36s
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Types of Data in AI Models | 2m 58s
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Exam Tips | 5m 46s
The Machine Learning Pipeline
35m 59s
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Exploring the Machine Learning Pipeline | 5m 2s
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What Is Feature Engineering? | 5m 20s
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Hyperparameters vs. Parameters | 7m 2s
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Metrics for Classification Models | 5m 44s
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Metrics for Regression Models | 5m 10s
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Fundamentals of ML Operations | 2m 16s
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Exam Tips | 5m 25s
AWS Managed AI/ML Services and Applications
25m 52s
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Introducing AWS AI/ML Services | 1m 44s
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Vision: Amazon Rekognition | 2m 34s
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Vision: Amazon Textract | 2m 3s
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Language: Amazon Comprehend | 3m 29s
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Speech: Amazon Polly | 3m 12s
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Speech: Amazon Transcribe | 3m 13s
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Chatbots: Amazon Lex | 2m 2s
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Forecasting: Amazon Forecast | 55s
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Personal Assistants: Amazon Kendra | 1m 20s
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Recommendations: Amazon Personalize | 2m 29s
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Exam Tips | 2m 51s
Unpacking Amazon SageMaker
15m 17s
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Introducing Amazon SageMaker | 3m 35s
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Amazon SageMaker Data Wrangler | 2m 12s
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Amazon SageMaker Feature Store | 1m 8s
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Demo: Amazon SageMaker Console Walkthrough | 2m 49s
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Amazon SageMaker Deployments | 2m 52s
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Exam Tips | 2m 41s
Exam Question Review
7m 1s
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Basic AI Concepts and Terminologies | 1m 43s
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The Machine Learning Pipeline | 1m 15s
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AWS Managed AI/ML Services and Applications | 2m 4s
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Unpacking Amazon SageMaker | 1m 59s
Generative AI Concepts
21m 19s
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Tokens and Chunking | 3m 23s
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Embeddings and Vectors | 2m 41s
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Transformer-based LLMs | 1m 49s
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Different Generative AI Models | 4m 33s
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Prompt Engineering | 1m 25s
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Foundation Model Lifecycle | 4m 24s
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Generative AI Concepts Review | 3m 4s
Use Cases for Generative AI
12m 28s
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Generative AI Use Cases | 2m 9s
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Advantages of Generative AI | 1m 38s
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Limitations and Challenges | 2m 14s
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Model Selection Factors | 2m 13s
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Business Metrics and Value | 2m 5s
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Generative AI Use Cases Exam Tips | 2m 9s
Generative AI in AWS
15m 14s
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Generative AI in AWS | 7m 12s
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Advantages of AWS Generative AI Services | 2m 35s
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Cost Tradeoffs of AWS Generative AI Services | 2m 20s
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AWS Generative AI Services Review | 3m 7s
Exam Question Review
8m 6s
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Generative AI Concepts | 3m 24s
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Disadvantages of Generative AI | 1m 25s
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AWS Generative AI Services | 1m 16s
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Cost Tradeoffs in AWS Generative AI Services | 2m 1s
Design Considerations for Foundation Model Applications
23m 4s
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Selecting Foundation Models | 3m 45s
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Inference Parameters | 4m 26s
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Retrieval-augmented Generation (RAG) | 2m 34s
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Vector Storage Solutions on AWS | 2m 53s
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Cost Tradeoffs for Customization | 2m 33s
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Agents for Multi-step Tasks | 2m 21s
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Exam Tips | 4m 32s
Prompt Engineering Techniques
15m 45s
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Fundamentals of Prompt Engineering | 3m 52s
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Prompt Engineering Techniques | 4m 1s
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Benefits and Best Practices | 3m
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Risks and Limitations of Prompt Engineering | 2m 15s
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Exam Tips | 2m 37s
Training and Fine-tuning Foundation Models
9m 53s
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Key Elements of Training Foundation Models | 2m 3s
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Methods for Fine-tuning Foundation Models | 2m 11s
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Preparing Data to Fine-tune a Foundation Model | 3m 1s
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Exam Tips | 2m 38s
Evaluating Foundation Model Performance
10m 25s
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Evaluation Approaches for Foundation Models | 2m 1s
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Performance Metrics | 4m 30s
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Business Objective Alignment | 1m 48s
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Exam Tips | 2m 6s
Exam Questions Review
6m 1s
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Design Considerations for Foundation Models | 1m 48s
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Prompt Engineering Techniques | 1m 20s
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Training and Fine-tuning Foundation Models | 1m 24s
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Evaluating Foundation Model Performance | 1m 29s
Responsible AI Systems
33m 40s
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Features of Responsible AI | 8m 14s
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Bias and Variance | 6m 22s
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Types of Bias | 2m 51s
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Transparency and Explainability in Models | 3m 33s
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Risks of GenAI Models | 3m 28s
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Human-centered Design for Explainable AI | 3m 14s
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Exam Tips | 5m 58s
Building Responsible AI with AWS Tools
21m 28s
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Explainability of Model Decisions with SageMaker Clarify | 2m 32s
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Automating Data Labeling with Amazon SageMaker Ground Truth | 2m 33s
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AI Governance | 5m 10s
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Boosting ML Accuracy with Amazon A2I | 2m 8s
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Using Guardrails in Amazon Bedrock for Fairness and Safety | 5m 43s
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Exam Tips | 3m 22s
Exam Question Review
4m 52s
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Responsible AI Systems | 3m 9s
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Building Responsible AI with AWS Tools | 1m 43s
Securing AI Systems
36m 37s
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IAM Roles and Policies | 4m 43s
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Services and Features for Securing AI Systems | 4m 27s
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Data History | 3m 40s
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Secure Data Engineering | 5m 23s
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AI System Security Risks and Threats | 4m 14s
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Application and Infrastructure Security for AI Systems | 7m 46s
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Exam Tips | 6m 24s
Governance and Compliance for AI Systems
33m 7s
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Regulatory Compliance Standards for AI Systems | 8m 48s
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AWS Services for GRC | 7m 3s
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AWS Dashboard Features for AI Security | 2m 14s
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Data Governance Strategies | 5m 38s
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Governance Protocols | 3m 45s
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Exam Tips | 5m 39s
Exam Questions Review
7m 10s
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Exam Questions Review | 7m 10s
About the authors
Noreen Hasan
Noreen Hasan has been a software engineer for over a decade in several industries ranging from startups to financial institutions. Her focus was on iOS mobile development, and in the last years her focus shifted to AWS and cloud computing. Her mantra has always been 'Building to Solve, Building to Empower' because solving problems and empowering others is the main reason she feels fulfilled in this field.
When she is not developing technical solutions, she enjoys swimming, Zumba, reading and listening to podcasts.
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Michael Cassidy
Michael Cassidy is a DevOps professional most interested in infrastructure as code. Specifically Terraform. He has a passion for building scalable and efficient production architectures. When not busy with technology, Michael loves coaching others on fitness and wellness. It's a passion of his to inspire people to lead healthier lifestyles and feel their best. Especially those in tech.
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Chris Jackson
Chris Jackson is a cybersecurity professional with years of experience in identifying security incidents, securing applications and security training. Over the years, he has tested web applications for vulnerabilities, helped deploy SIEM platforms and more. He is passionate about teaching cybersecurity and committed to learning new technologies.
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