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Mammoth Club All levels 16 sections 64 lectures

Generative AI on AWS - Get Certified as an AWS AI Practitioner in 7+ HOURS

Designed for aspiring AI professionals, developers, and cloud practitioners, this course gives you the skills and confidence to ace foundational certifications and contribute meaningfully to cutting-edge AI solutions. Ace the AWS Certified AI Practitioner exam and get certified!

01
Skill level
All levels
02
Sections
16
03
Lectures
64
04
Instructor
Alex Kropf
What's inside

This course includes.

16
Sections
4
Quizzes
Certificate of completion
Included
Mobile and desktop access
Included
AI learning assistance
Included
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Course content

Curriculum & lectures.

16 sections · 64 lectures
+ Welcome 2 lectures Preview
Exam Guide Locked
Submit a Question/Feedback Locked
+ Data Center Essentials for Certifications 1 lecture
Data center architectures and components (servers, networking, databases, apps and more) Locked
+ AWS Cloud Practioner Essentials for Certifications 5 lectures
What is AWS? Locked
Basic global infrastructure of the AWS Cloud Locked
Core AWS services (compute, network, databases, and storage) Locked
Basics of AWS Cloud migration Locked
Source Code Locked
+ Explain basic AI concepts and terminologies 5 lectures
01 Define basic AI terms (AI, ML, deep learning, neural networks, computer vision, NLP...) Locked
02 Describe the similarities and differences between AI, ML, and deep learning. Locked
03 Describe various types of inferencing (for example, batch, real-time). Locked
04 Describe the different types of data in AI models (labeled, unlabeled, tabular, time-series, imag Locked
05 Describe supervised learning, unsupervised learning, and reinforcement learning. Locked
+ Identify practical use cases for AI 5 lectures
01 Recognize applications where AI can provide value (decision making, solution scalability, automat Locked
02 Determine when ML solutions are not appropriate (cost-benefit analyses, specific outcome is neede Locked
03 Select the appropriate ML techniques for specific use cases (regression, classification, clusteri Locked
04 Identify examples of real-world AI applications (CV, NLP, speech recognition, recommendation syst Locked
05 Explain the capabilities of AWS managed AI services (SageMaker, Transcribe, Translate, Comprehend Locked
+ Describe the ML development lifecycle 6 lectures
01 Describe components of ML pipeline (data collection, EDA, pre-processing, feature engineering...) Locked
02 Understand sources of ML models (open source pre-trained models, training custom models) Locked
03 Describe methods to use a model in production (for example, managed API service, self-hosted API) Locked
04 Identify relevant AWS services and features for each stage of an ML pipeline (SageMaker, Data Wra Locked
05 Understand fundamental concepts of MLOps (experimentation, repeatable processes, scalable...) Locked
06 Understand model performance metrics and business metrics to evaluate ML models Locked
+ Explain the basic concepts of generative AI 3 lectures
01 Understand foundational generative AI concepts (tokens, chunking, embeddings, vectors...) Locked
02 Identify potential use cases for generative AI models (image, video, audio, summarization...) Locked
03 Describe the foundation model lifecycle (data selection, model selection, pre-training...) Locked
+ Understand the capabilities and limitations of generative AI for solving business problems 4 lectures
01 Describe the advantages of generative AI (for example, adaptability, responsiveness, simplicity) Locked
02 Identify disadvantages of generative AI solutions (for example, hallucinations, interpretability, Locked
03 Understand factors to select generative AI models (model types, performance requirements, capabil Locked
04 Determine business value and metrics for generative AI (performance, efficiency, conversion...) Locked
+ Describe AWS infrastructure and technologies for building generative AI applications 4 lectures
01 Identify AWS services and features to develop generative AI applications Locked
02 Advantagues of using AWS generative AI to build applications Locked
03 Understand the benefits of AWS infrastructure for generative AI applications Locked
04 Understand cost tradeoffs of AWS generative AI services Locked
+ Describe design considerations for applications that use foundation models 6 lectures
01 Identify selection criteria to choose pre-trained models Locked
02 Understand the effect of inference parameters on model responses Locked
03 Define Retrieval Augmented Generation (RAG) and describe its business applications Locked
04 Identify AWS services that help store embeddings within vector databases Locked
05 Explain the cost tradeoffs of various approaches to foundation model customization Locked
06 Understand the role of agents in multi-step tasks (for example, Agents for Amazon Bedrock) Locked
+ Choose effective prompt engineering techniques 4 lectures
01 Describe the concepts and constructs of prompt engineering Locked
02 Understand techniques for prompt engineering Locked
03 Understand the benefits and best practices for prompt engineering Locked
04 Define potential risks and limitations of prompt engineering Locked
+ Describe the training and fine-tuning process for foundation models 3 lectures
01 Describe the key elements of training a foundation model Locked
02 Define methods for fine-tuning a foundation model Locked
03 Describe how to prepare data to fine-tune a foundation model Locked
+ Describe methods to evaluate foundation model performance 2 lectures
01 Understand approaches to evaluate foundation model performance Locked
03 Determine whether a foundation model effectively meets business objectives Locked
+ Explain the development of AI systems that are responsible 7 lectures
01 Identify features of responsible AI (bias, fairness, inclusivity, robustness, safety, veracity) Locked
02 Use tools to identify features of responsible AI (Guardrails for Amazon Bedrock) Locked
03 Understand responsible practices to select a model (environmental considerations, sustainability) Locked
04 Identify legal risks of working with generative AI Locked
05 Identify characteristics of datasets Locked
06 Understand effects of bias and variance Locked
07 Describe tools to detect and monitor bias, trustworthiness, and truthfulness Locked
+ Recognize the importance of transparent and explainable models 4 lectures
01 Transparent and explainable models Locked
02 Understand the tools to identify transparent and explainable models Locked
03 Identify tradeoffs between model safety and transparency Locked
04 Understand principles of human-centered design for explainable AI Locked
+ Explain methods to secure AI systems 3 lectures
01 Identify AWS services and features to secure AI systems Locked
02 Understand the concept of source citation and documenting data origins Locked
03 Describe best practices for secure data engineering Locked
Description

About this course.

✅ Learn core AI terminology—understand the difference between AI, ML, deep learning, and neural networks

✅ Explore data types and inferencing models including batch and real-time approaches

✅ Dive into supervised, unsupervised, and reinforcement learning frameworks

✅ Master foundational generative AI concepts: transformers, diffusion models, embeddings, RAG, and more

✅ Understand the full lifecycle of foundation models—from data selection to fine-tuning and deployment

✅ Apply foundation models to real-world use cases like chatbots, summarization, code generation, and customer support

✅ Gain insight into performance metrics like BLEU, ROUGE, and BERTScore

✅ Make smart, cost-effective choices in model selection, customization, and inference parameters

✅ Secure your AI applications with AWS tools, IAM policies, encryption practices, and data lineage controls

✅ And much more!


🧠 Boost your learning experience with engaging tools—executable coding questions, flashcards, quizzes, and more—to help you effortlessly grasp and remember essential concepts.


🎁 Includes downloadable resources and lifetime access.


If you're ready to gain fluency in the language of AI, contribute to responsible innovation, and position yourself for high-impact roles in one of the world’s fastest-evolving tech frontiers—enroll now and transform your future.

Instructors

Taught by people who ship.

Alex Kropf

Alex Kropf

Mammoth Club's CLO, public speaker, consultant, IT author and Senior Software Developer. Alex has produced best-selling courses, books and workshops for Mammoth Club, Course Pro and our clients since 2016.

Ready to start building?

Designed for aspiring AI professionals, developers, and cloud practitioners, this course gives you the skills and confidence to ace foundational certifications and contribute meaningfully to cutting-edge AI solutions. Ace the AWS Certified AI Practitioner exam and get certified!

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