AWS Certified AI Practitioner AIF-C01

AWS Certified AI Practitioner AIF-C01.

Het AWS AI Practitioner (AIF-C01) certificaat is een van de nieuwste toevoegingen aan het aanbod van AWS-certificeringen. Het is bedoeld voor personen die een basiskennis willen opbouwen van kunstmatige intelligentie (AI) en machine learning (ML).
Je leert de belangrijkste AI- en ML-concepten, AWS AI/ML-diensten, verantwoorde AI-praktijken en praktische toepassingen van foundation modellen. Daarnaast doe je praktijkervaring op met AWS-diensten zoals Amazon SageMaker en Amazon Bedrock, en NLP-diensten zoals Amazon Comprehend en 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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Exclusive Exam

AWS Certified AI Practitioner AIF-C01

Date

You can start at any time! Please contact one of our training advisors.

Locations

Self-study

Learning method

E-Learning

Training duration: 5+ uur (excl. real live challance labs)

Language

English

Tip!

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