From the course: AWS Certified AI Practitioner (AIF-C01) Cert Prep
AWS Certified AI Practitioner (AIF-C01): Introduction - Amazon Web Services (AWS) Tutorial
From the course: AWS Certified AI Practitioner (AIF-C01) Cert Prep
AWS Certified AI Practitioner (AIF-C01): Introduction
- Hello, my name is Chad Smith, and I'd like to welcome you to my course, AWS Certified AI Practitioner, (AIF-C01) video course. As the title implies, this course covers the AIF-C01 version of the exam. AWS certifications are among the most sought after in the technology sector, and the certification exams cover a wide range of AWS services, strategies, and implementation patterns. The AI practitioner certification is an excellent foundation for those who want to work with machine learning or artificial intelligence, including generative AI. This course targets the certification exam, and will provide directed study of the exam topics in several helpful ways. First, it will cover the exam logistics, and detail the background knowledge needed to pass. Next, the course will explain all four question domains with examples, diagrams, and discussions from real world technology scenarios to help with knowledge retention. Finally, the lessons covering exam content contain many sample questions with answer strategies explained in detail. It's important to know why the correct answer is correct, but it's more important to know why the incorrect answers are incorrect. This knowledge improves the ability to narrow the field of answers when guessing is required. The videos cover sample exam questions and will provide a full discussion of the question and all answer choices. We'll explain how each answer choice addresses the requirements from the question, and then discover which answer is correct. This skill is especially important for this foundational exam where many questions are scenario-based, and there are many definitions to remember and the right answer choices only have slight differences between them. A little bit about me. My name is Chad Smith, and I've spent years working with AWS in various roles. I've engaged with AWS customers for system operations, security, and architecture. As an AWS architect and operations professional, I draw upon my own experiences and decisions made in the trenches to illustrate the concepts covered in this video course. Technology has always been my passion, and I find that AWS keeps me on my toes with the increasing pace of evolution of the ecosystem. I want to share this passion with you by bringing all of my excitement and energy into your AWS learning journey.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.
Contents
-
-
(Locked)
Module 2: Fundamentals of AI and ML introduction36s
-
(Locked)
Learning objectives31s
-
(Locked)
Basic AI terminology4m 52s
-
(Locked)
Introduction to machine learning6m 38s
-
(Locked)
Introduction to deep learning2m 47s
-
(Locked)
Question breakdown, part 12m 53s
-
(Locked)
Question breakdown, part 22m 40s
-
(Locked)
-
-
(Locked)
Learning objectives36s
-
(Locked)
ML pipeline components5m 11s
-
(Locked)
ML model sources and deployment types2m 44s
-
Introduction to MLOps3m 46s
-
(Locked)
AWS ML pipeline services4m 34s
-
(Locked)
ML model performance metrics3m 11s
-
(Locked)
Question breakdown, part 12m 34s
-
(Locked)
Question breakdown, part 22m 49s
-
(Locked)
-
-
(Locked)
Module 3: Fundamentals of generative AI introduction41s
-
(Locked)
Learning objectives28s
-
(Locked)
Basic generative AI terminology4m 8s
-
(Locked)
Generative AI use cases4m 14s
-
(Locked)
Foundation model lifecycle2m 35s
-
Question breakdown, part 12m 53s
-
(Locked)
Question breakdown, part 22m 32s
-
(Locked)
-
-
(Locked)
Module 4: Applications of foundation models introduction41s
-
(Locked)
Learning objectives34s
-
(Locked)
Pretrained model selection criteria5m
-
(Locked)
Model inference parameters3m 54s
-
Introduction to RAG5m 1s
-
(Locked)
Introduction to vector databases4m 15s
-
(Locked)
AWS vector database service3m 16s
-
(Locked)
Foundation model customization cost tradeoffs3m 16s
-
(Locked)
Generative AI agents5m 17s
-
(Locked)
Question breakdown, part 12m
-
(Locked)
Question breakdown, part 22m 50s
-
(Locked)
-
-
(Locked)
Learning objectives35s
-
(Locked)
Prompt workflow2m 42s
-
(Locked)
Prompt engineering concepts4m 43s
-
(Locked)
Prompt engineering techniques6m 16s
-
(Locked)
Prompt engineering best practices2m 33s
-
(Locked)
Prompt engineering risks and limitations3m 53s
-
(Locked)
Question breakdown, part 13m 48s
-
(Locked)
Question breakdown, part 22m 50s
-
(Locked)
-
-
(Locked)
Module 5: Responsible and secure AI solutions introduction46s
-
(Locked)
Learning objectives43s
-
(Locked)
Responsible AI features4m 8s
-
AWS responsible AI tools3m 41s
-
(Locked)
Responsible AI model selection practices3m 26s
-
(Locked)
Generative AI legal risks3m 25s
-
(Locked)
AI dataset characteristics2m 47s
-
(Locked)
AI bias and variance4m 54s
-
(Locked)
AWS AI bias detection tools2m 1s
-
(Locked)
Question breakdown, part 13m 18s
-
(Locked)
Question breakdown, part 23m 16s
-
(Locked)
-
-
(Locked)
Learning objectives39s
-
(Locked)
Transparency and explainability definitions3m 20s
-
(Locked)
AWS transparency and explainability tools3m 48s
-
(Locked)
AI model safety and transparency tradeoffs3m 21s
-
(Locked)
Human-centered AI design principles3m 37s
-
(Locked)
Question breakdown, part 13m 22s
-
(Locked)
Question breakdown, part 23m 54s
-
(Locked)
-
-
(Locked)
Learning objectives39s
-
(Locked)
AWS AI security services and features6m 1s
-
(Locked)
Data citations and origin documentation3m 27s
-
(Locked)
Secure data engineering best practices4m 54s
-
(Locked)
AI security and privacy considerations4m 56s
-
(Locked)
Question breakdown, part 12m 38s
-
(Locked)
Question breakdown, part 22m 47s
-
(Locked)