Artificial Intelligence (AI) enables machines to perform tasks requiring human-like intelligence, such as decision-making and problem-solving. Its subsets include Machine Learning (ML), which uses data to improve systems without explicit programming, Deep Learning (DL), which employs neural networks for advanced pattern recognition, and Generative AI (Gen AI), which creates new content like text and images by analyzing data. Together, these technologies drive innovation, streamline processes, and deliver personalized experiences, making them essential in today’s digital world.
The "Exam Prep AIF-C01: AWS Certified AI Practitioner" course is designed for individuals seeking to deepen their understanding of AI and machine learning technologies, both in general and within the AWS ecosystem. This course prepares candidates to earn the AWS Certified AI Practitioner certification.
The course features approximately 6.5 to 7 hours of video lectures, covering both theoretical concepts and hands-on exercises. It is organized into five modules, each further divided into lessons. To reinforce learning, each module includes assignments, quizzes, and in-video questions.
Enroll in the “Exam Prep AIF-C01: AWS Certified AI Practitioner” course today and take a step toward advancing your career!
- Module 1: Foundation Model and Generative AI on AWS
- Module 2: Fundamentals of AI & ML
- Module 3: AWS Managed AI Services
- Module 4: Prompt Engineering and Responsible AI
- Module 5: Secure AI Solutions
This course is designed for professionals seeking to demonstrate a comprehensive understanding of AI/ML, Generative AI, and related AWS services and tools, regardless of their job function.
By the end of the course, learners will be able to:
- Understand AI, ML, and Generative AI concepts both broadly and within AWS.
- Select suitable AI/ML technologies for use cases.
- Build Generative AI applications with AWS services.
- Apply responsible AI/ML practices.
- Secure Generative AI solutions with proper IAM rules.
Welcome to Week 1 of the Exam Prep AIF-C01: AWS Certified AI Practitioner course. In this week, we will be introduced to the features and use cases of Foundation Models and Generative AI models. We will learn about the RAG Architecture of LLM and implement it using Amazon Bedrock. By the end of the week, we will be able to understand Vector Embeddings, GuardRails, and Agents feature of Amazon Bedrock.
Identify the Potential Use Cases of Generative AI•7 minutes
Challenges of Generative AI•8 minutes
Components of Generative AI•4 minutes
Lifecycle of Foundation Models•7 minutes
Types of Foundation Models•5 minutes
Evaluation Metrics of Foundation models•9 minutes
Amazon Bedrock - Overview•9 minutes
Amazon Bedrock - Demo•6 minutes
Foundation Models on Amazon Bedrock•6 minutes
Understanding RAG Architecture of LLM•6 minutes
AWS Services for Storage of Vector Embeddings•9 minutes
Amazon Bedrock RAG & Knowledge Base - Demo•9 minutes
Amazon Bedrock - GuardRails•6 minutes
Amazon Bedrock - GuardRails - Demo•12 minutes
Amazon Bedrock Agents•4 minutes
PartyRock - Amazon Bedrock Playground•5 minutes
Amazon Bedrock - Pricing•5 minutes
5 readings•Total 50 minutes
Welcome to the Course•10 minutes
Overview of Foundation Models and Generative AI in AWS•10 minutes
GenAI Application Lifecycle •10 minutes
Retrieval Augmented Generation (RAG)•10 minutes
Amazon Bedrock•10 minutes
3 assignments•Total 80 minutes
Basics of Foundation Models and Generative AI - Knowledge Check•15 minutes
Amazon Bedrock and its features - Knowledge Check•15 minutes
Foundation Models and Generative AI in AWS - Assessment•50 minutes
1 discussion prompt•Total 10 minutes
Meet and Greet•10 minutes
Fundamentals of AI & ML in AWS
Module 2•2 hours to complete
Module details
Welcome to Week 2. This week, we will be introduced to the differences between AI, Deep Learning, and Machine Learning. We will learn Machine learning lifecycle, different types of data used in Machine learning, and machine learning techniques. At the end, we will explore MLOps and its related AWS services.
What's included
15 videos2 readings2 assignments
Show info about module content
15 videos•Total 81 minutes
What is Machine Learning?•5 minutes
AI Vs Deep Learning Vs Machine Learning•3 minutes
Types of Data in Machine Learning•8 minutes
Types of Machine Learning•5 minutes
Steps for Machine Learning•7 minutes
Data Preprocessing Essentials•6 minutes
Evaluating Classification Models•5 minutes
Confusion Matrix•4 minutes
Evaluation Metrics - Regression•6 minutes
Unsupervised Learning - Clustering•5 minutes
Types of Inferencing•9 minutes
What is Deep Learning ?•5 minutes
Usage of Deep Learning/ ML models in Production•5 minutes
What is MLOps ?•5 minutes
AWS Services for MLOps•4 minutes
2 readings•Total 20 minutes
Overview of Fundamentals of AI & ML in AWS•10 minutes
Machine Learning Development Lifecycle•10 minutes
2 assignments•Total 30 minutes
Machine Learning and MLOps - Knowledge Check•15 minutes
Fundamentals of AI & ML - Assessment•15 minutes
AWS Managed AI/ML Services
Module 3•3 hours to complete
Module details
Welcome to Week 3. This week, we will be introduced to AWS managed AI/ML services. We will learn to implement Amazon Comprehend, Amazon Translate, Amazon Transcribe, Amazon Polly, Amazon Rekognition, and, Amazon Augmented AI (A2I).We will be exploring features of Amazon SageMaker with its components. At the end of the week, we will learn Amazon Q, a generative AI–powered assistant developed by AWS.
What's included
24 videos4 readings3 assignments
Show info about module content
24 videos•Total 115 minutes
Amazon Comprehend•5 minutes
Amazon Comprehend - Demo•3 minutes
Amazon Translate•3 minutes
Amazon Translate - Demo•4 minutes
Amazon Transcribe•3 minutes
Amazon Transcribe - Demo•3 minutes
Amazon Polly•4 minutes
Amazon Polly - Demo•2 minutes
Amazon Rekognition•4 minutes
Amazon Rekognition - Demo•3 minutes
Amazon Lex•6 minutes
Amazon Lex - Demo•5 minutes
Amazon Kendra•5 minutes
Amazon Augmented AI (A2I)•4 minutes
Amazon Personalize•4 minutes
Amazon Textract•4 minutes
Introduction to Amazon SageMaker•4 minutes
Amazon Sagemaker - Demo•11 minutes
Amazon Sagemaker Data Wrangler - Deep Dive•7 minutes
Amazon Sagemaker Feature Store - Deep Dive•8 minutes
Amazon Sagemaker Model Monitor - Deep Dive•9 minutes
In the Week 4, we will be introduced to the concepts of Prompt Engineering and Responsible AI. We will learn different techniques to design effective prompts used to optimize generative AI model. We will also explore the key principles of Responsible AI and use them to select a generative AI model. By the end of the week, we will discover AWS services to select the model and guide them to produce the desired outputs.
Legal risks of working with generative AI•6 minutes
AWS Tools for Responsible AI•3 minutes
AWS Tools for Explainabe AI•4 minutes
3 readings•Total 30 minutes
Overview of Prompt Engineering and Responsible AI in AWS•10 minutes
Prompt Engineering•10 minutes
Responsible AI•10 minutes
2 assignments•Total 30 minutes
Effective Prompts and Responsible AI Principles - Knowledge Check•15 minutes
Prompt Engineering and Responsible AI - Assessment•15 minutes
Secure AI Solutions in AWS
Module 5•2 hours to complete
Module details
Welcome to Week 5. This week, we will be introduced to shared responsibility model in AWS to secure AI/ML solutions. We will learn to identify and apply security and privacy considerations for AI systems. By the end of the week, we will explore AWS services and features to assist with governance and security of AI solutions.
What's included
5 videos2 readings2 assignments
Show info about module content
5 videos•Total 38 minutes
AWS Services for Securing AI Systems•5 minutes
AWS Shared Responsibility Model•4 minutes
AWS Services for Governance of AI Applications•4 minutes
Practice Questions with explanation•21 minutes
Conclusion•4 minutes
2 readings•Total 20 minutes
Overview of Secure AI Solutions in AWS•10 minutes
What's Next?•10 minutes
2 assignments•Total 60 minutes
Project: Translate Text and Document Between Languages in the Cloud•45 minutes
Secure AI Solutions - Assessment•15 minutes
Instructor
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How much does AWS AI Practitioner Certification cost?
The cost of AWS Certified AI Practitioner certification is USD 100 for the exam.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.