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Catalog / All levels / AWS Certified AI Practitioner with 10 Practice Exams
Mammoth Club All levels 5 sections 65 lectures

AWS Certified AI Practitioner with 10 Practice Exams

Ready to move beyond AI buzzwords and build a real foundation in cloud-based AI?

MC
Created by Team Mammoth
·
65 lectures
01
Skill level
All levels
02
Sections
5
03
Lectures
65
04
Instructor
Team Mammoth
What's inside

This course includes.

✓
5
Sections
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content

Curriculum & lectures.

5 sections · 65 lectures
+ Chapter 1: Fundamentals of AI and ML 15 lectures Preview
1.1 Core AI and ML Terminology Locked
1.2 Comparing AI, ML, Deep Learning, and Generative AI Locked
1.3 Types of Inferencing Locked
1.4 Data Types in AI Models Locked
1.5 Learning Paradigms—Supervised, Unsupervised, and Reinforcement Learning Locked
1.6 Recognizing Where AI/ML Provides Value Locked
1.7 Determining When AI/ML Is Not Appropriate Locked
1.8 Selecting ML Techniques for Specific Use Cases Locked
1.9 Real-World AI Applications Locked
1.10 AWS Managed AI/ML Services Overview Locked
1.11 Components of an ML Pipeline Locked
1.12 Sources of ML Models and Production Methods Locked
1.13 AWS Services for the ML Pipeline Locked
1.14 Fundamentals of MLOps Locked
1.15 Model Performance and Business Metrics Locked
+ Chapter 2: Generative AI Concepts and AWS Services 12 lectures
2.1 Foundational GenAI Concepts — Tokens, Chunking, and Embeddings Locked
2.2 Foundational GenAI Concepts — Models and Architectures Locked
2.3 Use Cases for Generative AI Locked
2.4 The Foundation Model Lifecycle Locked
2.5 Advantages of Generative AI Locked
2.6 Limitations and Risks of Generative AI Locked
2.7 Selecting Generative AI Models Locked
2.8 Business Value and Metrics for GenAI Locked
2.9 AWS Services for Building GenAI Applications Locked
2.10 Advantages of AWS GenAI Services Locked
2.11 AWS Infrastructure Benefits for GenAI Locked
2.12 Cost Tradeoffs of AWS GenAI Services Locked
+ Chapter 3: Applications of Foundation Models 17 lectures
3.1 Selection Criteria for Pre-Trained Models Locked
3.2 Inference Parameters and Model Responses Locked
3.3 Retrieval Augmented Generation (RAG) Locked
3.4 Vector Databases and Embedding Storage on AWS Locked
3.5 Cost Tradeoffs of FM Customization Approaches Locked
3.6 Agents for Multi-Step Tasks Locked
3.7 Concepts and Constructs of Prompt Engineering Locked
3.8 Prompt Engineering Techniques Locked
3.9 Benefits and Best Practices for Prompt Engineering Locked
3.10 Risks and Limitations of Prompt Engineering Locked
3.11 Key Elements of Training Foundation Models Locked
3.12 Methods for Fine-Tuning Foundation Models Locked
3.13 Preparing Data for Fine-Tuning Locked
3.14 Approaches to Evaluate FM Performance Locked
3.15 Metrics for Assessing FM Performance Locked
3.16 Aligning FM Performance with Business Objectives Locked
3.17 Evaluating FM-Powered Applications Locked
+ Chapter 4: Responsible AI 11 lectures
4.1 Features of Responsible AI Locked
4.2 Tools for Identifying Responsible AI Features Locked
4.3 Responsible Practices for Model Selection Locked
4.4 Legal Risks of Working with Generative AI Locked
4.5 Characteristics of Responsible Datasets Locked
4.6 Effects of Bias and Variance Locked
4.7 Tools to Detect and Monitor Bias and Trustworthiness Locked
4.8 Transparent and Explainable Models vs. Black Box Models Locked
4.9 Tools for Identifying Transparent and Explainable Models Locked
4.10 Tradeoffs Between Model Safety and Transparency Locked
4.11 Human-Centered Design for Explainable AI Locked
+ Chapter 5: Security, Compliance, and Governance for AI 10 lectures
5.1 AWS Services and Features to Secure AI Systems Locked
5.2 Source Citation and Documenting Data Origins Locked
5.3 Best Practices for Secure Data Engineering Locked
5.4 Security Considerations for AI Systems Locked
5.5 AI-Specific Security Threats Locked
5.6 AWS Services for Governance and Compliance Locked
5.7 Data Governance Strategies Locked
5.8 Governance Policies and Review Processes Locked
5.9 Governance Frameworks, Standards, and Training Locked
Find Your Exams Locked
Description

About this course.

This course is your straightforward, exam-focused roadmap to passing the AWS Certified AI Practitioner certification and understanding how AI actually works in production environments.

â–ē Master Core AI & ML Concepts: Learn the fundamentals behind machine learning, generative AI, and data-driven systems

â–ē Adopt a Cloud-First AI Mindset: Understand how AWS approaches scalable, secure, and responsible AI solutions

â–ē Explore Real-World AWS AI Use Cases: See how AI services are applied across industries using practical scenarios

✔ī¸ Lifetime access to all learning modules

✔ī¸ 10 practice exams with unlimited retries

Enroll now and start your journey into AWS-powered AI!

Ready to start building?

Ready to move beyond AI buzzwords and build a real foundation in cloud-based AI?

Buy lifetime access →