Introduction to Generative AI – Art of the Possible
The Introduction to Generative AI – Art of the Possible course provides an introduction to generative AI, use cases, risks and benefits. With the help of a content generation example, we illustrate the art of the possible. By the end of the course, learners should be able to describe the basics of generative AI, its […]
Amazon Lex Getting Started
Course description Amazon Lex is a fully managed artificial intelligence (AI) service with advanced natural language models to design, build, test, and deploy conversational interfaces for voice and text. In this Getting Started course, you will learn about the benefits, features, typical use cases, technical concepts, and cost of Amazon Lex. You will review an […]
Building a Generative AI-Ready Organization
Course description Building a Generative AI-Ready Organization is the last course in a three-part series of Generative AI Essentials for Business and Technical Decision Makers. If you have not done so already, we recommend you start with the first course in the series, Introduction to Generative AI: Art of the Possible. By the end of the course, you should be able to describe the key considerations for building a generative AI-ready organization. You will be equipped with the tools and the knowledge to upskill employees and to infuse generative AI thinking in your workplace. • Course level: Beginner • Duration: 1 hour Activities This course includes interactive elements and text instruction. Course objectives In this course, you will learn how to: • Describe key concepts and strategies that you need to know to integrate generative AI into your organization • Describe how to build a generative AI-ready organization • Describe how to frame discussions with your employees and overcome the challenges you might face • Describe the importance of governance and organizational structure in implementing generative AI successfully Intended audience This course is intended for: • Business decision-makers Prerequisites This course is the last course in the Generative AI Essentials for Business and Technical Decision Makers series. We recommend you complete the first two courses in the series before taking this course. • Introduction to Generative AI: Art of the Possible • Planning a Generative AI Project Course outline Section 1: How to Use this Course Section 2: Introduction • Generative AI Organization Overview Section 3: Preparing Your Organization • Start with Your Leaders • Prepare Your Employees Section 4: Organizing for Success • Cloud Operating Model • Team Success • Establishing a Governance Model Section 5: Taking Action Now • Infusing Generative AI Thinking • Upskilling Employees in the Use of Generative AI Section 6: Wrap-up • Conclusion
Amazon Bedrock Getting Started
Course description: Amazon Bedrock is a fully managed service that offers leading foundation models (FMs) and a set of tools to quickly build and scale generative AI applications. The service also helps ensure privacy and security. In this Getting Started course, you will learn about the benefits, features, typical use cases, technical concepts, and cost of Amazon Bedrock. You will also review an architecture that uses Amazon Bedrock, along with other Amazon Web Services (AWS) offerings, to build a chatbot solution. Through a guided tutorial consisting of a narrated video, step-by-step instructions, and transcript, you will try Amazon Bedrock in your AWS account. ‐ Course level: Fundamental ‐ Duration: 1 hour Activities: This course includes presentations, graphics, and a step-by-step tutorial to follow along. Course objectives: In this course, you will learn to: ‐ Understand how Amazon Bedrock works. ‐ Familiarize yourself with basic concepts of Amazon Bedrock. ‐ Recognize the benefits of Amazon Bedrock. ‐ List typical use cases for Amazon Bedrock. ‐ Describe the typical architecture associated with an Amazon Bedrock solution. ‐ Understand the cost structure of Amazon Bedrock. ‐ Implement a demonstration of Amazon Bedrock in the AWS Management Console. Prerequisites We recommend that attendees of this course have completed the following training: ‐ AWS Technical Essentials Course outline ‐ Introduction to Amazon Bedrock ‐ Architecture and Use Cases ‐ How Do You Use Amazon Bedrock? ‐ Learn More
Planning a Generative AI Project
Course description Planning a Generative AI Project is the second course in the three-part series called Generative AI Essentials for Business and Technical Decision Makers. If you have not done so already, start with the first course in the series, Introduction to Generative AI – Art of the Possible. In this course, you will learn about the technical foundations and key terminology related to generative artificial intelligence (AI). You will explore the steps to planning a generative AI project, and evaluate the risks and benefits of using generative AI. Course level: Beginner • Duration: 1 hour Activities This course includes text instruction and illustrative graphics. Course objectives In this course, you will learn how to: • Discuss the technical foundations and key terminology for generative AI. • Explain the steps for planning a generative AI project. • Identify some of the risks and mitigations when using generative AI. Intended audience This course is intended for: • Business and technical decision makers Prerequisites This course is the second course in the Generative AI Essentials for Business and Technical Decision Makers series. It is recommended that you complete the first course in the series, Introduction to Generative AI – Art of the Possible, before taking this course. Course outline Section 1: Technical Foundations and Terminology for Generative AI • Generative AI Fundamentals • Generative AI in Practice • Generative AI Context Section 2: Planning a Generative AI Project • Steps in Planning a Generative AI Project Section 3: Evaluating the Use of Generative AI for Your Project • Risks and Mitigation • Conclusion
Amazon Kendra Getting Started
Course description: Amazon Kendra is a natural language search service that uses machine learning for improved accuracy in search results and the ability to search unstructured data. In this course, you will learn about the benefits, features, typical use cases, technical concepts, and costs of Amazon Kendra. You will review an architecture for a search solution using Amazon Kendra that you can further adapt to your use case. Through a guided tutorial consisting of a narrated video, step-by-step instructions, and transcript, you will also try the service in your own Amazon Web Services (AWS) account. • Course level: Fundamental • Duration: 1.5 hours Activities: This course includes presentations and a step-by-step tutorial to follow along. Course objectives: In this course, you will do the following: • Understand how Amazon Kendra works. • Familiarize yourself with basic concepts of Amazon Kendra. • Recognize the benefits of Amazon Kendra. • List typical use cases for Amazon Kendra. • Describe the typical architectures associated with an Amazon Kendra solution. • Specify what it would take to implement Amazon Kendra in a real-world scenario. • Understand the cost structure of Amazon Kendra. • Implement a demonstration of Amazon Kendra in the AWS Management Console. Prerequisites We recommend that attendees of this course have completed the following trainings: • AWS Technical Essentials Course outline • Introduction to Amazon Kendra • Architecture and Use Cases • How Do You Create an Index in Amazon Kendra? • How Do You Add a Data Source in Amazon Kendra? • How Do You Create an FAQ with Amazon Kendra? • How Do You Delete Amazon Kendra Resources?
Amazon Transcribe Getting Started
Course description: Amazon Transcribe is a fully managed artificial intelligence (AI) service that helps you convert speech to text using automatic speech recognition (ASR) technology. In this Getting Started course, you will learn about the benefits, features, typical use cases, technical concepts, and costs of Amazon Transcribe. You will review an architecture for a transcription solution using Amazon Transcribe that you can further adapt to your use case. Through a guided tutorial consisting of narrated video, step-by-step instructions, and transcripts, you will also try real-time and batch transcription in your own Amazon Web Services (AWS) account. Course level: Fundamental • Duration: 1.5 hours Activities: This course includes presentations, graphics, and a step-by-step tutorial to follow along. Course objectives: In this course, you will do the following: • Understand how Amazon Transcribe works. • Familiarize yourself with basic concepts of Amazon Transcribe. • Recognize the benefits of Amazon Transcribe. • List typical use cases for Amazon Transcribe. • Describe the typical architectures associated with an Amazon Transcribe solution. • Specify what it would take to implement Amazon Transcribe in a real-world scenario. • Understand the cost structure of Amazon Transcribe. • Implement a demonstration of Amazon Transcribe in the AWS Management Console. Prerequisites We recommend that attendees of this course have completed the following trainings: • AWS Technical Essentials Course outline • Introduction to Amazon Transcribe • Architecture and Use Cases • How Do You Create a Real-Time Transcription in the AWS Management Console? • How Do You Create a Batch Transcription in the AWS Management Console? • How Do You Create a Transcription Using a Custom Vocabulary?
Getting Started with Amazon Textract
Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents and goes beyond optical character recognition (OCR) to identify, understand, and extract data from forms and tables. In this Getting Started course, you will learn about the benefits, features, typical use cases, technical concepts, and costs of Amazon Textract. You will review an architecture for a text-extraction solution using Amazon Textract that you can further adapt to your use case. Through a guided tutorial, you will also try the service in your own Amazon Web Services (AWS) account. • Course level: Fundamental • Duration: 90 minutes Activities This course includes presentations, graphics, and a step-by-step tutorial to follow along. Course objectives In this course, you will do the following: • Understand how Amazon Textract works. • Familiarize yourself with basic concepts of Amazon Textract. • Recognize the benefits of Amazon Textract. • List typical use cases for Amazon Textract. • Describe the typical architectures associated with an Amazon Textract solution. • Specify what it would take to implement Amazon Textract in a real-world scenario. • Understand the cost structure of Amazon Textract. • Implement a demonstration of Amazon Textract in the AWS Management Console. Prerequisites We recommend that attendees of this course have completed the following trainings: • AWS Technical Essentials We also recommend that you review the following resources, if you are not already familiar with AWS Step Functions and AWS Cloud Development Kit (CDK): • Create a Serverless Workflow with AWS Step Functions and AWS Lambda • Getting started with the AWS CDK Course outline • Amazon Textract Basics • How Is Amazon Textract Used to Architect a Solution? • Amazon Textract Use Cases • Amazon Textract Guidelines and Best Practices • Amazon Textract Costs • Using Amazon Textract to Synchronously and Asynchronously Extract Text from Documents • Learn More about Amazon Textract
Introduction to Amazon SageMaker
Amazon SageMaker is a fully managed service that data scientists and developers use to quickly build, train, and deploy machine learning models. In this introductory course, you are given an overview of Amazon SageMaker, focused on the service’s three main components: notebooks, training, and hosting.
Introduction to Amazon Comprehend
This course introduces you to Amazon Comprehend, a new AWS service that helps with natural language processing. In this course, we discuss how Amazon Comprehend solves challenges like the exponential growth of unstructured text, explore the service’s five main capabilities, and review some popular use cases. We also demonstrate the service so you can see it in action.
The Elements of Data Science
Learn to build and continuously improve machine learning models with Data Scientist Harsha Viswanath, who will cover problem formulation, exploratory data analysis, feature engineering, model training, tuning and debugging, as well as model evaluation and productionizing.
Exam Readiness: AWS Certified Machine Learning – Specialty
This course prepares you to take the AWS Certified Machine Learning – Specialty exam, which validates your ability to design, implement, deploy, and maintain machine learning (ML) solutions. In this course, you’ll learn about the logistics of the exam and the mechanics of exam questions, and you’ll explore the exam’s technical domains. You’ll review core AWS services and key concepts for the exam domains: 1) Data Engineering 2)Exploratory Data Analysis 3)Modeling 4) Machine Learning Implementation and Operations You’ll also learn key test-taking strategies and will put them into action, taking multiple study questions. Once you’ve honed your skills, you’ll have the chance to take a quiz that will help you assess your areas of strength and weakness, so that you’ll know what to emphasize in your pre-exam studies. Course objectives: By the end of this course, you will be able to: •Identify your strengths and weaknesses in each exam domain so that you know what to focus on when studying for the exam •Describe the technical topics and concepts that make up each of the exam domains •Summarize the logistics and mechanics of the exam and its questions •Use effective strategies for studying and taking the exam Intended audience: This course is intended for: •ML practitioners who have at least one year of practical experience, and who are preparing to take the AWS Certified Machine Learning – Specialty exam Prerequisites: We recommend that attendees of this course have: •Proficiency expressing the intuition behind basic ML algorithms and performing basic hyperparameter optimization •Understanding of the ML pipeline and its components •Experience with ML and deep learning frameworks •Understanding of and experience in model training, deployment, and operational best practices [Enroll] (www.aws.training) Course outline: Module 0: Course Introduction: Module 1: Exam Overview and Test-taking Strategies: •Exam overview, logistics, scoring, and user interface •Question mechanics and design •Test-taking strategies Module 2: Domain 1 – Data Engineering: •Domain 1.1: Data Repositories for ML •Domain 1.2: Identify and implement a data-ingestion solution •Domain 1.3: Identify and implement a data-transformation solution •Walkthrough of study questions •Domain 1 quiz Module 3: Domain 2 – Exploratory Data Analysis: •Domain 2.1: Sanitize and prepare data for modeling •Domain 2.2: Perform featuring engineering •Domain 2.3: Analyze and visualize data for ML •Walkthrough of study questions •Domain 2 quiz Module 4: Domain 3 – Modeling: •Domain 3.1: Frame business problems as ML problems •Domain 3.2: Select the appropriate model(s) for a given ML problem •Domain 3.3: Train ML models •Domain 3.4 Perform hyperparameter optimization •Domain 3.5 Evaluate ML models •Walkthrough of study questions •Domain 3 quiz Module 5: Domain 4 – ML Implementation and Operations: •Domain 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance •Domain 4.2: Recommend and implement the appropriate ML services and features for a given problem •Domain 4.3: Apply basic AWS security practices to ML solutions •Domain 4.4: Deploy and operationalize ML solutions •Walkthrough of study questions •Domain 4 quiz Module 6: Additional Study Questions: •Opportunity to take additional study questions Module 7: Recommended Study Material: •Links to AWS blogs, documentation, FAQs, and other recommended study material for the exam Module 8: Course Wrap-up: •How to sign up for the exam •Course summary •Course feedback
