From the course: AWS Certified AI Practitioner (AIF-C01) Cert Prep

AWS generative AI services and features - Amazon Web Services (AWS) Tutorial

From the course: AWS Certified AI Practitioner (AIF-C01) Cert Prep

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AWS generative AI services and features

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- There are a number of helpful AWS services and features that can be used to help implement generative AI workloads. And let's take a look at some of those now. We're going to start off with SageMaker Jumpstart, and this gives you access to pre-trained models for tasks like summarization and image generation. You can choose to customize the models with your own data and then deploy that through the UI or the SDK. Organizations can share both models and notebooks across their various workloads. And the data is always encrypted and it stays within your own VPC so that you can ensure privacy and security. Next, we have Amazon Bedrock, and this is a service that's designed around providing high-performing foundation models from both Amazon and other companies from a single API. And you can choose to customize the models privately with your own data using fine-tuning or retrieval augmented generation, or RAG. It is entirely serverless, so you don't have to provide any infrastructure or extra operations to go along with it. And it allows for the secure integration of Gen AI into your own applications using AWS service API endpoints. Our next feature is also part of Bedrock, and it's called PartyRock. This is a playground for building generative AI apps. And you can create applications, you could generate jokes, personalized playlists, and it's all no code. And so this can help users learn AI in an area that is familiar and easy to work with and be able to use foundation models and chaining prompts together and other cool things. Next we have Amazon Q, and this is a generative AI assistant that is designed around the acceleration of software development as well as leveraging company data. There's Q Developer for coding tasks and Q Business for business intelligence tasks. And with code generation, you can answer questions, you can connect to other business tools, and it's extraordinarily useful. It also supports the ability to create AI apps that can integrate with QuickSight. And when you are asking questions around code generation or business intelligence, it can look at your current inventory of AWS resources to help provide more customized answers. And so let's put it all together. A retail company wants to automate various business tasks using AWS AI services. They want to improve efficiency as well as provide a better customer experience. And so we start with SageMaker Jumpstart. This is going to help the data science team. They can deploy and manage AI models to predict stock demands or optimize inventory. Bedrock is going to be used for integrating the Gen AI models and allows the company to personalize customer recommendations, create custom marketing campaigns, and these models can be fine-tuned with customer purchase history data. Next, we have PartyRock, and this is what the marketing team uses to create fun, interactive, AI-based web apps without having to write code. And this is something that's really important to allow for the creative exploration of the space. Then we have Q Developer, and this is used by the IT and engineering teams to generate and debug code for new business features such as an AI chatbot. And finally, Q Business is going to connect to the company's enterprise data, inventory, sales, customer feedback that is in different repositories, and then employees can query that and retrieve insights in a way that is easily understandable.

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