AWS Certified AI Practitioner AIF-C01 Training - OEM Certkit

The AWS AI Practitioner (AIF-C01) certificate is one of the latest additions to the AWS certifications shelf. It is designed for individuals looking to build foundational knowledge of artificial intelligence (AI) and machine learning (ML).
You’ll learn the key AI and ML concepts, AWS AI/ML services, responsible AI practices, and practical applications of foundation models. You’ll also gain hands-on experience with AWS services such as Amazon SageMaker and Amazon Bedrock and NLP services like Amazon Comprehend, and Rekognition.

Prerequisites:
None

Course outcome:

• Fundamentals of AI and ML
• Fundamentals of Generative AI
• Applications of Foundation Models
• Guidelines for Responsible AI
• Security, Compliance, and Governance for AI Solutions

Who should attend:
This course is intended for all professionals working with AWS.

Demo AWS Certified AI Practitioner AIF-C01 Training

CertKit content:

E-learning courses:

AWS AI Practitioner: Basic AI Concepts and Terminologies

Course: 25 Minutes

• Course Overview
• AI, ML, and Deep Learning
• Basic AI Terms
• Large Language Models (LLMs)
• Differences of AI, ML, and Deep Learning
• Inference Types
• Data Types of AI Models
• ML Methods
• Course Summary

AWS AI Practitioner: Practical Use Cases for AI

Course: 41 Minutes

• Course Overview
• Where AI/ML Application Provides Value
• When AI/ML Solutions Are Not Appropriate
• Machine Learning Techniques for Specific Use Cases
• Real-World AI Applications
• Using Amazon SageMaker
• Amazon Transcribe
• Amazon Translate
• Amazon Comprehend
• Amazon Lex
• Using Amazon Polly
• Course Summary

AWS AI Practitioner: The ML Development Lifecycle

Course: 30 Minutes

• Course Overview
• Components of a Machine Learning (ML) Pipeline
• Sources of ML Models
• Methods for Using a Model in Production
• SageMaker in an ML Pipeline
• Using SageMaker Data Wrangler in an ML Pipeline
• SageMaker Feature Store in an ML Pipeline
• SageMaker Model Monitor in an ML Pipeline
• Course Summary

AWS AI Practitioner: ML Operations (MLOps)

Course: 31 Minutes

• Course Overview
• Experimentation and Repeatable Processes
• Scalable Systems
• Technical Debt Management
• Achieving Production Readiness
• MLOps Model Monitoring and Retraining
• Accuracy and F1 Score
• Area Under the ROC Curve (AUC)
• Cost per User
• Development Costs
• Customer Feedback
• Return on Investment (ROI)
• Course Summary

AWS AI Practitioner: Basic Concepts of Generative AI

Course: 26 Minutes

• Course Overview
• Foundational Generative AI Concepts
• Foundational Generative AI Models
• Exploring Potential Use Cases for Generative AI Models
• The Foundation Model Lifecycle: Data Selection
• The Foundation Model Lifecycle: Model Selection
• The Foundation Model Lifecycle: Pre-Training
• The Foundation Model Lifecycle: Fine-Tuning
• The Foundation Model Lifecycle: Evaluation
• The Foundation Model Lifecycle: Deployment
• The Foundation Model Lifecycle: Feedback
• Course Summary

AWS AI Practitioner: Capabilities and Limitations of Generative AI

Course: 20 Minutes

• Course Overview
• Generative AI Advantage: Adaptability
• Generative AI Advantage: Responsiveness
• Generative AI Advantage: Simplicity
• Generative AI Disadvantage: Hallucinations
• Generative AI Disadvantage: Interpretability
• Generative AI Disadvantage: Inaccuracy
• Generative AI Disadvantage: Nondeterminism
• Appropriate Generative AI Model Selection
• Business Value and Metrics for Generative AI
• Course Summary

AWS AI Practitioner: Building Generative AI Applications with AWS

Course: 35 Minutes

• Course Overview
• Developing Generative AI Applications with Amazon SageMaker JumpStart
• Amazon Bedrock and Generative AI
• Developing Generative AI Applications with PartyRock Bedrock Playgrounds
• Generative AI Deployment with Amazon Q
• Advantages of Using AWS Generative AI Services
• Benefits of Using AWS Infrastructure for Generative AI Applications
• Cost Tradeoffs of Using AWS Generative AI Services
• Course Summary

AWS AI Practitioner: Design Factors for Applications Using Foundation Models

Course: 22 Minutes

• Course Overview
• Selection Criteria for Pre-Trained Models
• The Effect of Inference Parameters on Model Responses
• Retrieval-Augmented Generation (RAG)
• Storing Embeddings Within Vector Databases
• Cost Tradeoffs with Foundation Model Customization
• The Role of Agents in Multi-Step Tasks
• Course Summary

AWS AI Practitioner: Effective Prompt Engineering Techniques

Course: 23 Minutes

• Course Overview
• Prompt Engineering Concepts and Constructs
• Exploring Chain-of-Thought Prompt Engineering
• Zero-Shot Prompt Engineering
• Single-Shot and Few-Shot Prompt Engineering
• Using Prompt Engineering Prompt Templates
• Benefits and Best Practices of Prompt Engineering
• Potential Risks and Limitations of Prompt Engineering
• Course Summary

AWS AI Practitioner: Training, Fine-Tuning, and Evaluating Foundation Models

Course: 29 Minutes

• Course Overview
• Key Elements of Training a Foundation Model
• Methods for Fine-Tuning a Foundation Model
• Preparing Data to Fine-Tune a Foundation Model
• Evaluating Foundation Model Performance
• Metrics to Assess Foundation Model Performance
• Determining If a Foundation Model Meets Business Goals
• Course Summary

AWS AI Practitioner: Guidelines for Responsible AI

Course: 32 Minutes

• Course Overview
• Features and Tools of Responsible AI
• Responsible Practices and Legal Risks of Generative AI
• Characteristics of Datasets
• Effects and Tools for Bias and Variance
• Transparent, Explainable, Non-Transparent, and Non-Explainable Models
• Utilizing Tools to Identify Transparent and Explainable Models
• Tradeoffs Between Model Safety and Transparency
• Principles of Human-Centered Design for Explainable AI2
• Course Summary

AWS AI Practitioner: Security, Compliance, and Governance for AI Solutions

Course: 34 Minutes

• Course Overview
• Securing AI Systems and Services
• Source Citation and Documenting Data Origins
• Secure Data Engineering
• Security and Privacy Considerations for AI Systems
• Regulatory Compliance Standards for AI Systems
• Services to Assist with Governance and Regulation Compliance
• Data Governance Strategies
• Processes to Follow Governance Protocols
• Course Summary

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