AWS Certified AI Practitioner AIF-C01 - Hands On, In Depth!

Sundog Education by Frank Kane and Frank Kane

EARLY ACCESS. This course will be updated as more information about the exam is released by Amazon.

Get certified by Amazon for your knowledge of artificial intelligence and machine learning. It's hard to imagine a certification that would carry more weight in today's era of generative AI.

The AWS Certified AI Practitioner (AIF-C01) exam isn't just for developers - it's aimed at a wide variety of roles in the technology space. Whether you're a PM, manager, sales or marketing professional, or developer - the concepts behind artificial intelligence, GenAI, and machine learning (ML) aren't as hard as you think. This course starts with the basics, explaining things in plain English and with simple examples. No coding required.

We will go deep for those who want it, though. Hands-on activities will give you practice in building a custom chatbot using Amazon Bedrock and Knowledge Bases, building Guardrails for AI safety, and building a fully-fledged LLM agent armed with tools to extend an AI application - all just using the AWS console on the web. And demo videos walk you through training and using machine learning models using Amazon SageMaker.

Unlike other AWS certifications, there is a big business focus on this one. In addition to how the tech works, we'll also review best practices for the entire AI and machine learning lifecycle, the dimensions of "responsible AI," and best practices for security and governance with AI.

Some topics we'll cover include:

Fundamental concepts and terminologies of AI, ML, and Generative AI
How deep learning and large language models (LLM's) work
Evaluating and measuring AI models
Machine learning design principles
Machine learning operations (MLOps)
Use cases of AI, ML, and GenAI
Prompt engineering
Building machine learning pipelines with SageMaker
Common machine learning algorithms
Building generative AI applications with Amazon Bedrock
Retrieval Augmented Generation (RAG)
LLM Agents
Amazon Q Business, Q Developer, and Q Apps
High-level AWS AI and machine learning services
Model training and fine-tuning techniques
Responsible AI
AI Governance and Security

Although this is a course about AI, it is not AI-generated. You'll learn from a real human instructor, with real human experience in building AI applications in a professional setting.

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Instructor

Hey, I'm Frank Kane. I spent nine years working for Amazon from the inside as a senior engineer and senior manager, and I'm best known for my top-selling courses in "big data", data analytics, machine learning, AI, Apache Spark, system design, and Elasticsearch.

I've been teaching on Udemy since 2015, where I've reached over 850,000 students all around the world.

I've worked hard to keep this course up to date with the latest developments in AWS's machine learning and artificial intelligence technologies, and to make sure you're prepared for the latest version of this exam. Let's dive in and get you ready.

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This course also comes with:

Lifetime access to all future updates
9+ hours of video training
80 quiz questions to assess your readiness
A responsive instructor in the Q&A Section
Udemy Certificate of Completion Ready for Download
A 30 Day "No Questions Asked" Money Back Guarantee.

Join us in this course if you want to pass the AWS Certified AI Practitioner exam and master the world of AI and machine learning on the AWS platform.

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What's inside

Learning objectives

What to expect on the aws certified ai practitioner aif-c01 exam
Artificial intelligence, generative ai, and machine learning fundamentals
Machine learning design principles
Use cases for ai, generative ai (genai) and machine learning
Building machine learning systems and mlops with sagemaker

Building generative ai systems with amazon bedrock
Amazon q business and q developer
Model training and fine-tuning techniques with aws
Evaluating and measuring your machine learning models
Responsible ai, security, and governance

What to expect on the aws certified ai practitioner aif-c01 exam
Artificial intelligence, generative ai, and machine learning fundamentals
Machine learning design principles
Use cases for ai, generative ai (genai) and machine learning
Building machine learning systems and mlops with sagemaker
Building generative ai systems with amazon bedrock
Amazon q business and q developer
Model training and fine-tuning techniques with aws
Evaluating and measuring your machine learning models
Responsible ai, security, and governance

Syllabus

Introduction

Udemy 101

Get Your Copy of the Slides

Understand the relationship between AI and ML, how machine learning systems are trained, and how deep learning and generative AI works.

Taxonomy of Artificial Intelligence and Machine Learning Techniques

Supervised Learning Techniques

Evaluating Supervised Machine Learning with Train/Test and Cross Validation

Unsupervised and Self-Supervised Learning; Reinforcement Learning

The Bias / Variance Tradeoff

A Taxonomy of Machine Learning Techniques

Intro to Natural Language Processing (NLP)

History of Deep Learning; How Neural Networks Work

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

The Transformer Architecture and Self-Attention: How Generative AI Works

Generative Adversarial Networks (GANs)

Diffusion Models

Test your knowledge on this section on AI and Machine Learning fundamentals.

Apply best practices to the machine learning lifecycle and machine learning operations (MLOps.) Match AI and ML techniques with real-world use cases.

Machine Learning Design Principles and Lifecycle

Business Goal Identification

Framing the Machine Learning Problem

Data Processing

Model Development, Training, and Tuning

Deployment

Monitoring

The AWS Well-Architected Machine Learning (ML) Lens

Machine Learning Ops (MLOps)

AI Use Cases

Computer Vision Use Cases

Generative AI Use Cases

Test your knowledge of this section on ML design principles and use-cases.

Apply best practices in crafting prompts for generative AI to produce quality responses.

Benefits of Prompt Engineering

Anatomy of a Prompt

Prompt Best Practices

Types of Prompts

Avoiding Prompt Mis-Use and Mitigating Bias

Test your knowledge in this section on prompt engineering.

Apply SageMaker to machine problem lifecycles, end to end.

Set up an AWS Billing Alarm

Overview of Amazon SageMaker

Data Processing, Training, and Deployment with SageMaker

SageMaker Studio, SageMaker Debugger, SageMaker Experiments

SageMaker Autopilot

SageMaker Model Monitor and SageMaker Clarify

SageMaker Deployment Safeguards (and other features)

SageMaker Feature Store

SageMaker Lineage Tracking

SageMaker Data Wrangler

DEMO: SageMaker Studio, SageMaker Canvas, SageMaker Data Wrangler

Linear Learner, XGBoost, Seq2Seq

DeepAR, BlazingText, Obj2Vec, Object Detection

Image Classification, Semantic Segmentation, Random Cut Forest, NTM, LDA

KNN, K-Means, PCA, Factorization Machines, IP Insights

DEMO: Training and Inference with SageMaker and XGBoost

Test your knowledge on Amazon SageMaker's features and usage.

Build generative AI systems with Bedrock, SageMaker, and Amazon Q

Generative AI with Foundation Models and SageMaker JumpStart

Introduction to Amazon Bedrock

HANDS ON with the Bedrock Playground for Chat, Text, and Image Generation

Fine-Tuning Foundation Models with Bedrock

Bedrock Knowledge Bases and Retrieval-Augmented Generation (RAG)

HANDS ON with Bedrock Knowledge Bases

Bedrock Guardrails

HANDS ON with Bedrock Guardrails

LLM Agents and Bedrock Agents

HANDS ON with Bedrock Agents

More Bedrock Features

Amazon Q Developer (formerly CodeWhisperer)

Amazon Q Business

Amazon Q Apps and Pricing

Test your knowledge on Bedrock, RAG, Agents, and Amazon Q.

Apply purpose-built services for AI tasks from AWS

Amazon Comprehend

Amazon Translate

Amazon Transcribe

Amazon Polly

Amazon Rekognition

Amazon Forecast

Amazon Lex

Amazon Personalize

Additional AI and ML Services

Test your knowledge on AWS's purpose-built AI services.

Apply specific training and fine tuning techniques to ML models, and measure their resulting performance.

Fine-Tuning Foundation Models

Reinforcement Learning from Human Feedback (RLHF)

Preparing Data for Fine Tuning

Evaluation Techniques for Foundation Models

ROUGE, BLEU, and BERTscore metrics for LLM's

Choosing a Generative AI Evaluation Strategy

Machine Learning Model Evaluation: Precision, Recall, F1, RMSE

ROC Curves, AUC, P-R Curves

Review your learnings from this section on model training, tuning, and evaluation.

Design AI systems and applications with security, governance, and responsible best practices applied.

Dimensions of Responsible AI, and AWS Tools for Responsible AI

Best Practices for Responsible AI

AI Governance and Service Cards

Responsible Model Selection; Responsible Agency

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides foundational knowledge of AI and ML, making it suitable for those starting out

Covers various aspects of AI and ML, from fundamentals to practical applications

Includes hands-on activities to provide practical experience in building AI applications

Focuses on business applications of AI and ML, making it relevant for professionals in various roles

Emphasizes responsible AI, governance, and security, which are essential considerations in the industry

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AWS Certified AI Practitioner AIF-C01 - Hands On, In Depth! with these activities:

Follow tutorial on Neural Networks

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This tutorial can provide you with a solid foundation in Neural Networks

Browse courses on Neural Networks

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Search and find an accessible tutorial on Neural Networks
Work through the tutorial

Practice Python coding exercises

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This practice will improve your programming skills

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Solve daily coding exercises on Leetcode or other websites
Participate in coding challenges

Join a study group for this course

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This activity will help you to understand the concepts better

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Find a study group or create one with your classmates
Meet regularly and discuss the course material

Two other activities

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Show all five activities

Attend a Machine Learning workshop

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This can provide you with the opportunity to learn from experts

Browse courses on Machine Learning

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Search for a Machine Learning workshop in your area
Register and attend the workshop

Create a presentation on Machine Learning topics

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This activity will help you to identify important concepts and consolidate your knowledge

Browse courses on Machine Learning

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Choose a topic from Machine Learning
Research and gather information on the topic
Create a PowerPoint presentation using visuals, examples, and clear explanations

Career center

Learners who complete AWS Certified AI Practitioner AIF-C01 - Hands On, In Depth! will develop knowledge and skills that may be useful to these careers: