Course: Generative AI on AWS

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.

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.

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.

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.

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.

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.

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.