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Mastering AI on AWS

Training AWS Certified AI-Practitioner

Vivian Aranha

This comprehensive course, "Mastering AI on AWS: Training AWS Certified AI Practitioner" is designed to equip you with the knowledge and skills to excel in AI and machine learning using AWS services. Whether you're a cloud professional, developer, or AI enthusiast, this course will guide you through the fundamentals of AI and machine learning while providing hands-on experience with cutting-edge AWS AI services like Amazon SageMaker, Rekognition, Comprehend, Polly, and more.

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This comprehensive course, "Mastering AI on AWS: Training AWS Certified AI Practitioner" is designed to equip you with the knowledge and skills to excel in AI and machine learning using AWS services. Whether you're a cloud professional, developer, or AI enthusiast, this course will guide you through the fundamentals of AI and machine learning while providing hands-on experience with cutting-edge AWS AI services like Amazon SageMaker, Rekognition, Comprehend, Polly, and more.

Starting with foundational concepts of AI and machine learning, you’ll progress through practical labs, working with real-world applications such as image and video recognition, natural language processing, and recommendation systems. The course will also cover security best practices, responsible AI, and preparing for the AWS Certified AI Practitioner exam. By the end, you’ll be ready to build, deploy, and monitor AI applications on AWS and confidently pass the certification exam.

Through engaging lessons, hands-on projects, and practical exercises, this course ensures you develop both theoretical knowledge and practical skills to succeed in the growing field of AI and machine learning.

What you'll learn:

  • Fundamental concepts of AI, machine learning, and AWS AI services.

  • How to build and deploy AI applications using Amazon SageMaker, Rekognition, Comprehend, Polly, and more.

  • Best practices for securing AI and machine learning workflows on AWS.

  • How to prepare for and pass the AWS Certified AI Practitioner exam.

 

Who this course is for:

  • Cloud professionals wanting to expand into AI/ML.

  • AI/ML enthusiasts looking to gain practical skills using AWS services.

  • Aspiring data scientists and developers seeking to implement real-world AI solutions.

  • Students and professionals preparing for the AWS Certified AI Practitioner exam.

Enroll now

What's inside

Learning objectives

  • Understand key concepts of ai and machine learning on aws
  • Master aws ai and machine learning services
  • Build and deploy ai-powered applications on aws
  • Prepare for the aws certified ai practitioner exam

Syllabus

By the end of Section 1, students will understand the core concepts of AI, machine learning, and AWS AI services, and be familiar with the AWS Certified AI Practitioner exam structure and key topics.
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What will we Cover
Overview of AWS AI and ML Services
Importance of AI in Cloud Computing
Introduction to AWS Certified AI Practitioner Exam
Key Concepts: AI, Machine Learning, and Deep Learning
Prerequisites and Exam Preparation Strategy
By the end of Section 2, students will understand key machine learning algorithms, distinguish between supervised and unsupervised learning, and apply basic model training and evaluation techniques.
What will we cover
Supervised vs Unsupervised Learning
Key Machine Learning Algorithms
Training vs Inference in Machine Learning
Introduction to Model Evaluation and Performance
Hands-On Lab: Training a Simple Machine Learning Model
By the end of Section 3, students will be able to identify and use various AWS AI services, understand their use cases, and navigate the AWS environment to implement basic AI solutions.
Amazon SageMaker Overview
AWS AI Services for Vision, Speech, Language, and Recommendations
Introduction to AWS AI Service Use Cases
AI and ML Decision-Making Process on AWS
Hands-On Lab: Exploring AWS AI Services
By the end of Section 5, students will be able to implement NLP tasks using AWS services like Amazon Comprehend, Transcribe, & Translate for text analysis, speech recognition, & language translation.
Overview of NLP and its Applications
Amazon Comprehend: Sentiment Analysis, Entity Recognition, & Language Detection
Amazon Transcribe: Speech-to-Text Transcription
Amazon Translate: Real-Time Language Translation
Hands-On Lab: Analyzing Text Data with Amazon Comprehend
By the end of Section 6, students will be able to use AWS services like Amazon Rekognition and Textract to analyze images, videos, and extract text from documents for computer vision applications.
Introduction to Computer Vision on AWS
Amazon Rekognition: Image and Video Analysis
Amazon Textract: Extracting Text from Documents
Hands-On Lab: Image and Video Processing with Amazon Rekognition
By the end of Section 7, students will be able to create voice interfaces using Amazon Polly and perform automatic speech recognition tasks using Amazon Transcribe for real-time speech applications.
Amazon Polly: Text-to-Speech Conversion
Amazon Transcribe: Automatic Speech Recognition
Building Real-Time Speech Interfaces on AWS
Hands-On Lab: Building a Voice Interface with Amazon Polly
By the end of Section 8, students will be able to implement security best practices for AI and machine learning workflows on AWS, including data encryption, compliance, and monitoring.
Security and Compliance in AWS AI Services
Data Encryption and Security in Machine Learning Workflows
Monitoring and Logging in SageMaker and AWS AI Services
Hands-On Lab: Implementing Security Best Practices for AI Services
By the end of Section 9, students will be able to create personalized recommendation systems using Amazon Personalize and understand its applications in industries like e-commerce and media.
Introduction to Amazon Personalize
Building Recommendation Engines
Use Cases: E-commerce, Media, and Healthcare
Hands-On Lab: Creating a Personalized Recommendation System
By the end of Section 10, students will be able to apply AWS AI and ML services to real-world use cases across various industries, leveraging AI solutions to address specific business challenges.
AI in Healthcare, Finance, Retail, and Manufacturing
Real-World Examples of AWS AI Services in Production
Case Studies: Successful AI and ML Projects on AWS
Group Discussion: Best Practices for AI Deployment
By the end of Section 11, students will be able to apply AWS guidelines for responsible AI, ensuring fairness, mitigating bias, and developing ethical AI models for real-world applications.
Importance of Ethics in AI
AWS Guidelines for Responsible AI Use
Fairness, Bias, and Interpretability in AI Models
Hands-On Lab: Ensuring Fairness and Mitigating Bias in AI Models
By the end of Section 12, students will be able to prepare for the AWS Certified AI Practitioner exam, having mastered key concepts, completed mock tests, and developed effective exam strategy
What will we cover here
AWS Certified AI Practitioner Exam Structure and Scoring
Key Exam Topics and Concepts to Focus On
Practice Exam Questions and Sample Tests
Time Management and Exam Day Strategies
Final Exam Preparation Checklist

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Provides hands-on experience with AWS AI services like Amazon SageMaker, Rekognition, Comprehend, and Polly, which are essential for building and deploying AI applications on AWS
Covers security best practices for AI and machine learning workflows on AWS, including data encryption, compliance, and monitoring, which are crucial for secure AI deployments
Includes hands-on labs that allow learners to work with real-world applications such as image and video recognition and natural language processing, which reinforces practical skills
Explores responsible AI, ensuring fairness, mitigating bias, and developing ethical AI models, which is increasingly important in AI development and deployment
Teaches how to create personalized recommendation systems using Amazon Personalize, which is valuable for industries like e-commerce and media seeking to enhance user experiences
Requires familiarity with AWS services, which may necessitate prior experience or additional introductory coursework for those new to the AWS ecosystem

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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 Mastering AI on AWS: Training AWS Certified AI-Practitioner with these activities:
Review Machine Learning Fundamentals
Reinforce your understanding of core machine learning concepts before diving into AWS-specific implementations. This will help you grasp the underlying principles and apply them effectively within the AWS ecosystem.
Browse courses on Machine Learning
Show steps
  • Review key concepts like supervised and unsupervised learning.
  • Practice basic model training and evaluation techniques.
  • Familiarize yourself with common machine learning algorithms.
Review 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'
Solidify your understanding of machine learning fundamentals with a practical guide. This book will provide hands-on experience with popular machine learning libraries, complementing the AWS-focused content of the course.
View Hands-On Machine Learning with Scikit-Learn,... on Amazon
Show steps
  • Read the chapters covering supervised and unsupervised learning.
  • Work through the code examples to build and train models.
  • Experiment with different algorithms and hyperparameters.
AWS AI Services Exploration
Familiarize yourself with the AWS Management Console and practice navigating to different AI services. This will improve your efficiency and confidence when working with AWS AI tools during the course.
Show steps
  • Log in to the AWS Management Console.
  • Explore the Amazon SageMaker interface.
  • Navigate to Rekognition, Comprehend, and Polly services.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a Blog Post on AWS AI Use Cases
Deepen your understanding of AWS AI services by researching and writing about real-world use cases. This will help you connect the theoretical concepts to practical applications and improve your communication skills.
Show steps
  • Research different industries and their AI needs.
  • Identify specific use cases where AWS AI services can be applied.
  • Write a blog post explaining the use cases and benefits.
Build a Sentiment Analysis Application
Apply your knowledge of Amazon Comprehend to build a practical sentiment analysis application. This project will solidify your understanding of NLP concepts and AWS AI services.
Browse courses on Sentiment Analysis
Show steps
  • Collect a dataset of text data (e.g., product reviews).
  • Use Amazon Comprehend to analyze the sentiment of the text.
  • Visualize the sentiment analysis results.
Review 'AWS Certified Machine Learning Specialty Complete Video Course and Practice Exam'
Prepare for the AWS Certified AI Practitioner exam by reviewing a dedicated study guide. This will help you identify key concepts and practice answering exam-style questions.
View AWS Certified Big Data Specialty 2019 - In... on Amazon
Show steps
  • Watch the video lectures covering key exam topics.
  • Take the practice exams to assess your knowledge.
  • Review the explanations for incorrect answers.
Answer Questions on AI Forums
Reinforce your understanding by helping others learn. Answering questions on AI forums will force you to articulate your knowledge and identify any gaps in your understanding.
Show steps
  • Find online forums related to AI and AWS.
  • Browse the forums for unanswered questions.
  • Provide helpful and accurate answers to the questions.

Career center

Learners who complete Mastering AI on AWS: Training AWS Certified AI-Practitioner will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models, and this course is directly relevant to this role. This course emphasizes hands-on experience with AWS AI services like SageMaker, Rekognition, and Comprehend, which are essential tools for a machine learning engineer. Through this course, you gain practical skills in training, evaluating, and deploying machine learning models, including understanding key algorithms, model training, and performance evaluation. Additionally, you will learn to apply these skills to real-world applications such as image recognition and natural language processing, which are common tasks handled by a machine learning engineer. This course also covers security best practices, a critical aspect of deploying AI applications, making it an excellent preparatory step for a machine learning engineer.
See salaries and explore the career path for Machine Learning Engineer
AI Developer
An AI Developer focuses on creating applications that use artificial intelligence. This course will help an aspiring AI developer by providing comprehensive training in using AWS AI services, which are essential for creating AI-powered applications. You will learn to build and deploy AI applications using tools like Amazon SageMaker, Rekognition, and Polly, which are core parts of an AI developer's toolkit. The course also covers how to use AWS services for natural language processing, speech recognition, and image analysis, all of which are highly relevant to the day-to-day tasks of an AI developer. By the end of the course, you will be well-equipped to implement practical AI solutions on the AWS platform, which aligns perfectly with the AI developer role.
See salaries and explore the career path for AI Developer
Cloud Solutions Architect
A Cloud Solutions Architect designs and implements cloud computing solutions, and this course will be especially useful for those who want to specialize in AI and machine learning on AWS. The course covers key AWS AI services and how to use them in real-world applications, which is fundamental knowledge for designing AI solutions in the cloud. You will learn how to use Amazon SageMaker, Rekognition, and other services to build and deploy AI-powered applications, and you will familiarize yourself with security best practices, which are integral to the cloud solutions architect role. Furthermore, understanding how to implement AI security measures and best practices for AI deployment, will provide the know-how necessary for a cloud solutions architect working with AI-based solutions.
See salaries and explore the career path for Cloud Solutions Architect
Data Scientist
A Data Scientist analyzes data to derive insights and build predictive models. This course on AI on AWS will help a data scientist who wants to leverage the cloud for machine learning projects. The course includes training in machine learning algorithms, model training, and evaluation techniques, all of which are critical skills used by data scientists. The hands-on experience with Amazon SageMaker and other AWS services will help you in building and deploying machine learning models. You will also learn about natural language processing and computer vision, which will expand your skill set for various data analysis applications. This course will help a data scientist by providing practical skills in using AWS to improve their data modeling capabilities.
See salaries and explore the career path for Data Scientist
AI Consultant
An AI Consultant advises organizations on implementing AI solutions, and this course can help you gain the necessary technical understanding. This course provides a comprehensive overview of AWS AI services, including Amazon SageMaker, Rekognition, and Comprehend. It also covers the practical aspects of using these tools in real-world applications. An AI consultant must understand real-world applications of AI and machine learning, and this course teaches you to apply AWS AI services to various industries. You will gain a deeper understanding of not only the technical aspects but also the best practices for deploying secure and responsible AI solutions. This course provides the knowledge and abilities necessary for an AI consultant.
See salaries and explore the career path for AI Consultant
Computer Vision Engineer
A Computer Vision Engineer develops systems that enable computers to 'see' and interpret images and videos. This course has sections dedicated to computer vision using AWS services such as Amazon Rekognition and Textract, making it useful for an aspiring computer vision engineer. You will learn how to use these AWS services for image and video analysis, as well as how to extract text from documents, which are all essential for different computer vision applications. You'll also have practical experience through hands-on labs that are focused on processing images and videos with AWS tools. Through this course, you will learn how to work with computer vision systems on AWS, a skill of great use to a computer vision engineer.
See salaries and explore the career path for Computer Vision Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer builds systems that allow computers to understand and process human language. Specific sections in this course focus on natural language processing with AWS services like Amazon Comprehend, Transcribe, and Translate. The course provides essential training in how to use these AWS services for tasks such as sentiment analysis, speech-to-text transcription, and real-time language translation. You will also get hands-on experience with analyzing text data using Amazon Comprehend, which would make you more prepared for a career as a natural language processing engineer. You will gain crucial skills to process and interpret textual data, which a natural language processing engineer will use in their work.
See salaries and explore the career path for Natural Language Processing Engineer
Cloud Engineer
A Cloud Engineer is responsible for implementing and managing cloud computing systems, and this course can be helpful for a cloud engineer who wants to expand their understanding of AI on AWS. The course covers a variety of AWS AI and machine learning services that are highly relevant to cloud-based AI applications. The course emphasizes hands-on experience with AWS tools such as SageMaker and Rekognition, and through lectures and practical labs, you'll become familiar with critical concepts of AI, machine learning, and AWS AI services. This course helps a cloud engineer become more adept with AI, which is an increasingly important aspect of cloud computing. This course provides a robust understanding of cloud-based AI solutions.
See salaries and explore the career path for Cloud Engineer
AI Product Manager
An AI Product Manager oversees the development and launch of AI-based products, and this course provides a solid foundation for understanding the underlying technologies. This course gives you a thorough overview of AI concepts, machine learning, and the application of AWS AI services. You will gain insight into the capabilities of various tools such as Amazon SageMaker, Rekognition, and Polly, which is critical for making informed product decisions. Through this course you will gain enough background knowledge about AWS based AI solutions to be able to effectively drive product development, which makes it valuable for an AI product manager. Furthermore, the course discusses the ethical dimensions of AI, which enhances the critical thinking needed for a product manager.
See salaries and explore the career path for AI Product Manager
Data Analyst
A Data Analyst interprets data and identifies trends, and this course can help them understand how AI and machine learning can be leveraged in their analyses. The course provides foundational knowledge of machine learning algorithms and how to apply them using AWS services. You'll learn about data analysis techniques using Amazon Comprehend for natural language processing and Rekognition for computer vision. This will expand your skill set and enable you to use AI for more sophisticated data analysis and predictions. The hands-on labs will let you experiment with these tools, adding relevant practical experience, making this a very useful course for a data analyst.
See salaries and explore the career path for Data Analyst
Solutions Architect
A Solutions Architect designs and implements technological solutions for businesses, and this course may be helpful for a solutions architect who wants to understand AWS AI services. This course covers fundamental concepts of AI and machine learning, and introduces key AWS services, such as SageMaker and Rekognition. You'll learn to use these services for various tasks, such as building and deploying AI applications. The course also discusses security best practices. This course will provide some of the necessary technical understanding of AI on AWS that can improve your ability to make good judgements as a solutions architect.
See salaries and explore the career path for Solutions Architect
Machine Learning Specialist
A Machine Learning Specialist focuses on the theoretical and practical aspects of machine learning, and this course may be of interest. You will learn about key machine learning concepts, algorithms, and best practices around model training, all of which are highly relevant for this role. While the course is primarily focused on practical application using AWS services, this may still help improve a machine learning specialist's skills for development and deployment of AI models. This course will also cover a range of topics, including natural language processing and computer vision, adding to the specialist's skill set.
See salaries and explore the career path for Machine Learning Specialist
Software Developer
A Software Developer writes and tests software, and this course may be useful for those who want to incorporate AI into their applications. The course includes training on how to build and deploy AI-powered applications with AWS services like Amazon SageMaker, Rekognition, and Polly. You will gain practical experience in using AWS tools for natural language processing, speech recognition, and image analysis. It will provide some basic insights in AI integration. This can potentially open up new development opportunities, and may be beneficial to a software developer.
See salaries and explore the career path for Software Developer
Technical Project Manager
A Technical Project Manager oversees technical projects, and this course may help them understand the technical requirements of AI and machine learning projects on AWS. You will learn the fundamental concepts of AI, machine learning, and AWS AI services. This course also introduces the use of different AWS services such as Amazon SageMaker and Rekognition. While this course is more technical, it may be useful to a Technical Project Manager to help understand timelines and resourcing requirements for AI based projects on AWS, making it a potentially helpful course to take.
See salaries and explore the career path for Technical Project Manager
IT Manager
An IT Manager oversees information technology operations and may find this course useful for understanding AI and machine learning trends. This course covers fundamental AI and machine learning concepts and introduces key AWS services such as SageMaker and Rekognition. While this course is primarily technical, it may still help an IT manager make informed decisions about adopting AI technologies within their organization. It will give them a basic understanding of cloud-based AI solutions, which may help inform their work.
See salaries and explore the career path for IT Manager

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Mastering AI on AWS: Training AWS Certified AI-Practitioner.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a comprehensive introduction to machine learning concepts and tools. It is helpful for understanding the practical aspects of machine learning, including model building, training, and evaluation. It is commonly used as a textbook at academic institutions and by industry professionals. This book adds depth to the course by providing a strong foundation in machine learning algorithms and techniques.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
AWS Certified Big Data Specialty 2019 - In Depth...
Save
This video course and practice exam provide a comprehensive review of the topics covered in the AWS Certified Machine Learning Specialty exam. It is helpful for preparing for the exam and identifying areas where you need to improve your knowledge. It is commonly used by professionals preparing for the AWS certification. adds breadth to the course by providing a focused review of the exam content.
AWS Certified Big Data Specialty 2019 - In Depth...
Paperback
Check
AWS Certified Big Data Specialty 2019 - In Depth...
Kindle Edition
Check

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Effort
4 total hours
Via
Udemy
Instructor
Vivian Aranha
Language
English
Good to know
Provides hands-on experience with AWS AI services like Amazon SageMaker, Rekognition, Comprehend, and Polly, which are essential for building and deploying AI applications on AWS
Covers security best practices for AI and machine learning workflows on AWS, including data encryption, compliance, and monitoring, which are crucial for secure AI deployments
Includes hands-on labs that allow learners to work with real-world applications such as image and video recognition and natural language processing, which reinforces practical skills
Explores responsible AI, ensuring fairness, mitigating bias, and developing ethical AI models, which is increasingly important in AI development and deployment
Teaches how to create personalized recommendation systems using Amazon Personalize, which is valuable for industries like e-commerce and media seeking to enhance user experiences
Requires familiarity with AWS services, which may necessitate prior experience or additional introductory coursework for those new to the AWS ecosystem
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