Course: Generative AI on AWS

$279.00
$337.59 incl. vat

duration: 14 hours |

Language: English (US) |

access duration: 180 days |

Details

In this course, you will learn how to develop and deploy generative AI solutions using Amazon Web Services (AWS). The course begins with an introduction to the basic principles of generative AI and the benefits AWS provides for its development and deployment. You will explore basic models and their importance in AI startups, as well as AWS' generative AI services. The course also covers Amazon SageMaker for designing, training, optimizing, and deploying generative models, with hands-on tutorials on GANs, VAEs, and other architectures.

In addition, the course delves into Amazon Bedrock and explores its applications in generative AI projects, including the design of business applications. You will also learn about Amazon CodeWhisperer for automating code generation, Amazon Polly for text-to-speech applications, and Amazon Lex for building advanced chatbots. Finally, the course emphasizes best practices for using AWS tools to build scalable, secure, and efficient generative AI applications.

Result

After this course, you will have gained more knowledge about:

  • The principles of generative artificial intelligence (AI) and AWS's focus on GenAI.
  • The benefits of using AWS to develop and deploy generative AI.
  • The foundational models developed by AWS and their popularity among top AI startups.
  • How foundational models work and power generative models.
  • The differences between AWS AI and generative AI services.
  • How to analyze different types of generative models, such as GANs and VAEs, and compare their architectures.
  • How to design and implement a GAN using Amazon SageMaker.
  • SageMaker's tools for data preprocessing, model training, optimization, transfer learning, and deployment.
  • Amazon Bedrock and its role in GenAI development, API usage, and business application design.
  • Amazon CodeWhisperer, focusing on code generation automation, setup, and best practices.
  • Amazon Polly for text-to-speech applications, including its features, customization options, and API actions.
  • Amazon Lex for building conversational interfaces, with a focus on chatbot design, deployment, and integration with Amazon Polly.
  • AWS scalable architecture and best practices for deploying generative AI models, including the security and compliance measures AWS provides for AI systems.

Prerequisites

Experience in the field is a plus.

Content

Generative AI on AWS

14 hours

Generative AI on AWS: Getting Started

Amazon Web Services (AWS) provides both AI and generative AI services that can be used for developing and deploying business solutions. In this course, we will begin by introducing the foundational principles of generative artificial intelligence (GenAI). We will explore the benefits AWS offers for generative AI development and deployment. Next, we will delve into the intricacies of foundational models, focusing on their pivotal role in top AI startups and their relationship with generative models. Then, we will explore generative AI services available from AWS, distinguishing them from standard AI offerings, and gaining insight into the nuances of building generative AI systems on AWS. Finally, we will look at the robust infrastructure AWS provides to support generative AI development.

Generative AI on AWS: Building GenAI Models with Amazon SageMaker

This comprehensive course introduces learners to the world of generative artificial intelligence (AI) models within machine learning (ML), focusing on Amazon SageMaker as a prime tool for design, training, optimization, deployment, and monitoring. In this course, we will begin with a deep dive into the fundamental concepts and types of generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs). We will explore their pros, cons, and relevant architectures. Next, we will use hands-on tutorials and in-depth discussions to guide you through the steps of designing a GAN architecture, leveraging SageMaker's built-in algorithms, preprocessing data, and distributed training capabilities. We will then delve into optimization techniques, transfer learning, and quality evaluation methods, looking at ways apply these concepts in real-world scenarios. Lastly, we will introduce deployment strategies in SageMaker, highlighting how to serve models as endpoints for real-time inferences and how to efficiently monitor and troubleshoot their generative models.

Generative AI on AWS: Building GenAI Models with Amazon Bedrock

Dive deep into the world of Amazon Bedrock and examine its applications in generative artificial intelligence (GenAI) development. Starting with the core concepts, this course takes you on a comprehensive journey through the various ways Amazon Bedrock can be applied and the inherent benefits of using it for generative AI development. You will explore the Bedrock console and playgrounds and learn how to design and develop different generative AI models. Then you will focus on evaluating a generative AI project. Finally, you will design a business application using Bedrock and investigate the platform's strengths and weaknesses. At the end of the course, you will be equipped with the skills to design innovative business applications and develop new generative AI models using Amazon Bedrock.

Generative AI on AWS: Generating Code with Amazon CodeWhisperer

In the rapidly evolving world of software development, efficiency and productivity are paramount. This course provides an in-depth exploration of Amazon CodeWhisperer, one of the industry's most cutting-edge tools for automating the code generation process. You will begin by investigating the advantages of artificial intelligence (AI) code generation and its impact on productivity. Then you will explore Amazon CodeWhisperer, focusing on its role in automating code generation, its different techniques for code generation, specific use cases, and key features. Then you will set up CodeWhisperer for various platforms, including JetBrains and JupyterLab. Next, you will examine the code suggestions features of CodeWhisperer and learn how to configure security scans. Finally, you will discover how CodeWhisperer can optimize software development and you will identify best practices for generating and integrating code with CodeWhisperer.

Generative AI on AWS: Generating Speech with Amazon Polly

Delve into the groundbreaking capabilities of Amazon Polly in revolutionizing text-to-speech applications across diverse sectors. Starting with the basics of Polly's text-to-speech technology, you will discover how to preprocess input texts to ensure clarity and effectiveness in speech output. You will learn how to install, design, and use Polly's rich set of voices and languages to create dynamic and engaging audio content for various purposes, from audiobooks to customer service chatbots. Then you will explore Polly's customization options, including adjusting speech rate, pitch, and emphasis to produce natural-sounding speech that fits your specific needs. Next, you will investigate security, speech synthesis, and how to implement Speech Synthesis Markup Language (SSML) tags to add pauses, adjust pronunciation, and control the volume of the speech output, enhancing the listening experience. Finally, you will examine best practices for managing and optimizing your Polly usage to keep costs down while maintaining high-quality speech output.

Generative AI on AWS: Creating Intelligent Chatbots

Dive into the transformative world of Chatbots and their pivotal role in revolutionizing customer interactions. This course deciphers the intricacies of Amazon Lex, a premier tool for crafting conversational interfaces, and its seamless integration with Amazon Polly for lifelike speech. Participants will navigate through the creation of intent-driven, context-aware chatbots, delve deep into advanced capabilities like sentiment analysis and multi-turn conversations, and harness Amazon Polly for dynamic user responses. Moreover, attendees will learn to integrate external services to elevate chatbot functionality, ensuring the end products are not only interactive but also adhere to the highest standards of security, privacy, and compliance.

Generative AI on AWS: Deploying & Managing GenAI Services on AWS

Embark on an enlightening journey into the realm of Generative artificial intelligence (AI) and its transformative impact across diverse industries, powered by AWS's robust suite of services. This course unravels the optimal AWS tools for deploying generative AI models, guiding participants through the nuances of configuration, setup, and deployment options. Engage with best practices for data preparation, model training, and optimization using premier AWS services like Amazon SageMaker. Further, participants will refine their expertise in implementing scalable architectures, leveraging AWS's advanced monitoring tools, ensuring rigorous security protocols, and mastering performance optimization strategies. All these culminate in crafting efficient, scalable, and secure generative AI applications on AWS.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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