Build your first generative AI application with Amazon Bedrock

All right, good afternoon, everybody. Today is the first day of 2023 AWS re:Invent and welcome to this breakout session, "Build Your First Generative AI Application with Amazon Bedrock".

My name is She Ding, a senior AI/ML specialist solutions architect at AWS. Today together with me is my co-speaker Kasab Kki. Kasab is the principal product manager at Amazon Bedrock team at AWS.

In this session, we hope to give you a good understanding of generative AI fundamentals as well as a comprehensive overview of Amazon Bedrock. So we're gonna get started with a brief introduction to generative AI, followed by a feature walkthrough of Amazon Bedrock together with a demo. So we hope to give you some good understanding about how Amazon Bedrock addresses some of the challenges in building generative AI applications.

Next, we'll talk about some use cases and the customer stories to give you insights about how you can use Amazon Bedrock to build a generative AI application. And the last we're gonna end up this session with some guidance to show you how you can get started on your generative AI journey on AWS.

Now, as you, as you can see, there's a ton of exciting content in this session. So now let's get started.

As all of you are probably aware, there are a lot of things going on in the field of technology. Recently, the transformative and innovative technologies enable us to solve really complex problems and reimagine how we do things. So one of those transformative technologies that gains a lot of traction these days is generative AI.

Why a lot of attention has been given to how consumers are using generative AI? We believe there's an even bigger opportunity in how businesses will use it to deliver amazing experience to their customers and employees. The true power of generative AI goes beyond a search engine or a chatbot. It will transform every aspect of your company and organizations operate.

The field of artificial intelligence has been around for decades. Now you may have this question with so much potential. Why is this technology? The generative AI which has been percolating for decades reaching its tipping point now? Because with the massive proliferation of data, the availability of a highly scalable computer capacity and the advancement of machine learning technology over time. Generative AI is finally taking shape.

So what is exactly generative AI? Generative AI is an AI technology that can produce new original content which is close enough to human generated content for real world use cases such as text summarization, question answering and the image generation. The models powering generative AI are known as foundation models. They are trained on massive amounts of data with billions of parameters.

Developers such as yourself can adapt these models for a wider range of use cases with very little fine tuning needed. Using these models can reduce the time for developing new AI applications to a level that was really possible before.

Now, you may be wondering how generative AI fits within the world of artificial intelligence. So for anyone who's embarking on their generative AI journey for the first time, I know this might be confusing. So let's start with the broadest area.

First, the broadest area is artificial intelligence. Artificial intelligence is any technique that allows computers to mimic human intelligence. This can be through logic if there are statements or machine learning.

Within the scope of artificial intelligence, machine learning is a subset where machines are used to search for patterns in data to build the logic models automatically. Machine learning models develop from shallow models to deeply multi-layered neural networks. And these deeply multi-layered neural networks are called as deep learning models.

So deep learning models perform more complicated tasks like speech and image recognition. Generative AI is a subset of machine learning of deep learning. It is powered by foundation models that are extremely large and can perform most complex tasks.

Now let's dive a little deeper to understand the complexity of generative AI. Traditional formula of machine learning models allow us to take simple inputs like numerical values and map them to simple outputs like predicted values. With the advent of deep learning, we could take complicated inputs like images or videos and map them to relatively simple outputs like identifying an object in an image.

And now with generative AI, we can leverage massive amounts of complex data to capture and present the knowledge in a more advanced way, mapping complicated inputs to complicated outputs like extracting a summary and the key insights from a large document.

So this is how traditional machine learning models, deep learning models and the generative AI models under the scope of artificial intelligence differ from each other from input and output point of view.

Now let's take a look at the differences from a model point of view. Most traditional machine learning models are trained by supervised learning process. So these models use the architectures that require the data labeling and model training to produce one model for one specific task.

However, the foundation models that power generative AI application are driven by a transformer based on neural network architecture. So this architecture enables models to be portrayed a massive amount of unlabeled data by self supervisor learning process.

So these models can be used for a wide variety of generalize tasks and they can be easily adapted for a specific domain or industry by a customization process known as fine tuning with very small amount of label data.

So this difference between the traditional machine learning models and the foundation models is what can save you tons of time and effort.

There are mainly three types of foundation models. First type is text to text models. So these models accepted text as input and output text as well. For example, you can input text from a newspaper article to these models and get a text summary as a response.

Second category of foundation models is text to embedding models. These models accepted text as input and output numerical representations of the input text. So these numerical representations are called embeddings. We're gonna cover embeddings in a little more detail later in this session.

And the third category of foundation models is multimodal foundation models. So these models take text as input but generate output in another data modality such as images.

Now if you are able to follow along so far, so you basically officially understand the basics of generative AI.

Now let's walk you through what has been. Amazon's generated AI journey so far.

Machine learning innovation is in Amazon's DNA. We have been working on artificial intelligence and machine learning for over 20 years. Our e-commerce recommendation engine is driven by machine learning. The paths that optimize robotic picking routines at our fulfillment centers are driven by machine learning. In our supply chain forecasting capacity planning are all informed by machine learning.

Generative AI is not really new in Amazon. We have been doing generative AI for years. For example, we use it to deliver highly accurate search results on amazon.com. We use it to deliver the humanlike conversational experience by Alexa with which thousands of people, millions of people are enjoying every day.

At AWS, we have played a key role in democratizing machine learning and making it accessible to anyone who wants to use it. Now, more than 100,000 customers of all sizes and industries are using AWS for machine learning.

Customers have asked us how they can quickly begin using generative AI within their businesses and organizations to drive new levels of productivity and transform their offerings. Keeping these requirements in mind, we have identified four most important considerations to quickly build and deploy generative AI applications at scale.

All of the generative AI capabilities that we already have on AWS and that will be rolled out in the future are built based on these considerations at AWS. You have the easiest way to build generative AI applications with leading foundation models on the most performance and a low cost infrastructure.

You can use your data to fine tune these models and we always ensure that all your data in a secured and a private environment. We also provide you out of box applications and services to increase your productivity and shorten the time to market.

Now. I'm going to hand you over to my colleague Kasab and Kasab gonna talk about some of those challenges in building generative AI applications and how Amazon Bedrock addresses these challenges.

No Kasab, thank you She.

Building generated AI applications is challenging. Customers have told us that there are a few big things standing in their way of using foundation models more broadly.

First, there is no single optimized model for every task and models are going to be constantly improved with newer advances in technology for the foreseeable future. So for any particular use case, customers often need to put together several models that work with each other or upgrade the same model to newer versions. This can take time and resources.

Second, customers want it to be easy to take the base foundation model and build differentiated apps using their own data. And since the data that customers want to use for customization is incredibly valuable intellectual property, they needed to be completely protected, secure and private during that process. And they want control over how their data is shared and used foundation models.

On their own are also limited because they cannot complete complex tasks that require them to interact with external systems to make these kind of capabilities. Possible. Developers must go through a multi step process to accomplish even simple tasks tasks like booking a flight, filing an insurance claim or returning a purchased item. This process involves providing specific definitions and instructions to the foundation model, configuring access to company data sources and writing API code to execute actions.

Lastly, our customers have told us that they want application integration to be seamless without having to manage huge clusters of infrastructure or incur large costs to address all of these challenges.

We announced the general availability of Amazon Bedrock on 28th of September this year. Amazon Bedrock is a fully managed service that offers a choice of high performing foundation models from leading AI companies.

"Along with this, you also get broad set of capabilities that you need to build generative AI applications which simplifies development while maintaining privacy and security with Amazon Bedrock's comprehensive capabilities.

You can easily experiment with a variety of top foundation models, customize them privately with your data using techniques such as fine tuning and retrieval, augmented generation or RAG and create managed agents that execute complex business tasks from booking travel and processing insurance claims to creating ad campaigns and managing inventory all without writing any code.

Now, since Amazon Bedrock is service, you don't have to manage any infrastructure and you can securely integrate and deploy generative applications using the AWS services you are already familiar with.

Now you may be wondering how does Amazon Bedrock work? Well, first you pick a foundation model from the range of models that are provided by Amazon Bedrock. Next, you can customize the experience using your data through techniques such as fine tuning and retrieval, augmented generation. Then you can orchestrate your tasks using tools offered by Bedrock to solve those complex problems that you are working on.

The key point to remember here is that with Amazon Bedrock, your content is not used to improve the base models and is not shared with third party model providers.

Now, let's go through each of these steps one by one. We believe in offering the best choice and flexibility of foundation models for your use cases. We have our own Amazon TITAN models which includes TITAN Text for text summarization and our TITAN Embeddings model for embeddings and search.

We also offered Anthropic, AI2 Labs, Jurassic-2 models which are used for natural language to generate text in a variety of different languages. Anthropic CLAD models are built with the latest research on responsible AI to perform conversational and text processing tasks.

Command's Cohere large language model is a text generation model trained for business applications such as summarization, copywriting, dialogue extraction, and question answering fine tune versions of LAMA-2 models from Meta are ideal for dialogue use cases.

Bedrock also supports Stability AI's foundation models including the widely popular Stable Diffusion model which is capable of generating unique images, art and design.

Now, once you've selected your foundation model, one of the important capabilities that Bedrock has is the ability to customize a model and tailor it to your business, your data and your products.

You can fine tune Bedrocks foundation models by providing a few 100 labeled examples. You can do this by pointing Bedrock to your data sets in your S3 bucket, making it possible to create an accurate customized model for your specific problem without the burden of collecting large volumes of annotated data.

Now, while foundation models are incredibly powerful and have a robust understanding of natural language, like I've mentioned before, they require a lot of manual programming to complete complex tasks like booking a flight or processing an insurance claim. That's because the out of the box foundation models are not able to access up to date knowledge sources like your recent company specific data, they're also unable to take specific actions to fulfill those users requests.

So to make this happen, developers need to follow a number of resource intensive steps, steps like defining instructions and orchestration, configuring the foundation model to access company data sources and writing more custom code to execute these steps through a series of API calls.

Finally, developers must set up cloud policies for data security while also managing the infrastructure for the foundation model powered application. Now, all of these, as you can imagine takes weeks to address these challenges.

We offer Agents for Amazon Bedrock. Now with a few clicks, Agents for Amazon Bedrock can configure your foundation model to automatically break down and orchestrate tasks. And all of this can be done without writing any manual code.

The agent securely connects to your company's data sources through a simple API it automatically converts your data into a machine readable format and augments the user's request with relevant information to generate a more accurate response.

Now, agents in Bedrock also take actions to fulfill the user's request by executing API calls on your behalf. And you don't have to worry about complex systems integration and infrastructure provisioning as a fully managed service Agents for Bedrock takes care of all of this for you.

Now, you may remember that Sherri had mentioned the concept of embeddings. You know, this was earlier in the context of types of foundation models. Let me explain this concept a little bit further.

So vector embeddings are numerical representations of your text, image, audio and video data. While humans can understand the meaning and context of words, machines only understand numbers. So we have to translate them into a format that's suitable for machine learning by assigning a number to different feature of each word.

We can view vectors in a multidimensional space and measure the distance between them words that are related in context will have vectors that are closer together, which helps machines understand the similarities and differences between those words.

Now, for instance, cat is closer to kitten while dog is closer to puppy. So by comparing embeddings in this way, the model will produce more relevant and contextual responses than simple word matching.

Now, while embeddings aren't new for machine learning based applications, their importance is growing rapidly, especially with the availability of generative AI and natural language processing in general.

For example, embeddings can superpower semantic search for use cases like rich media search and product recommendations. In this scenario, you can see that the semantic search is greatly enhancing the accuracy of our outputs for a simple query like bright color golf shoes.

Now, in addition to semantic search embeddings can also be used to augment your prompts for more accurate results through a technique called retrieval augmented generation or RAG.

Now, let me try to explain RAG in a few sentences if I can RAG is an artificial intelligence technique that allows foundation models to incorporate information from a knowledge repository for generating all kinds of responses.

RAG allows you to customize foundation model responses without the overhead that is required with techniques like fine tuning. Although the degree of customization may not be as much as fine tuning. If your use case can be addressed using RAG, you may, you may not need to put in the effort required to perform fine tuning.

So Amazon Bedrock offers a feature called Knowledge Base. Now Knowledge Base lets you perform RAG without having to do any of the heavy lifting. All you have to do is point the foundation model to a data source.

We currently support three most commonly used data sources used for RAG applications. Once you've pointed the foundation model to the data source, the foundation model will start responding with the help of the knowledge response repository.

The foundation model will also provide citations that clarify which documents in the knowledge repository were used to generate the response citations are a good way to fact check the foundation models, responses and reduce the problems associated with what you've probably heard about hallucinations.

Now, let's take a quick look at a demo that shows both agents and knowledge base in action.

Now, in this example, we're going to see an example of a travel company that has built a bot for their users. We're gonna walk through all the different kinds of inputs that the board can take.

So now this is the the website of the company, they've created a chat board. The user is interfacing with the agent.

The user is asking for, you know, a recommendation for a family vacation, right? So all of this information is now being pulled up by the foundation model and it's able to give a very succinct summary of that recommendation.

And now the the user is asking for more relevant information like what are there any special events happening? What's happening recently? And then it's the users also asking what are the offers and discounts that are happening right now, this side, this kind of information can only be accessed through your data source.

But the but the agent is able to link to those kind of information and is able to respond based on that. It's also able to tell the lowest price for a room, it's able to give you that from up to date and detailed information.

And then finally, when the user says, hey, I want to book that room, it's actually able to do that booking itself. So that is basically the final step, which is not just serving the information, but making those API calls on your behalf to make sure that that action that the customer wants to do actually goes through.

Now, let's take a look at how you would have gone about configuring that exact experience.

Ok. So we're going to pull up the Amazon Bedrock console and we're gonna look at look at two steps. The first step is setting up the knowledge base itself.

So here's the, the Bedrock console in the console. I'm going to the knowledge base section. I'm going to create a knowledge base here. I'm gonna give it a name, I'm gonna, you know, sort of explain what this knowledge base is for it.

And then I'm gonna point it to the data source right here. I'm gonna point it to an S3 bucket and I'm going to select an embeddings model. In this case, I've selected a Titan image embeddings, Titan embeddings model and then I'm selecting one of the data sources that I want the embeddings to be stored in.

Once I've done that I come to the review page and then as soon as I hit, create pretty much that's it. Your embeddings have been created.

The next step now is to go from the knowledge base to the agent itself. So now we're gonna create the agent and give it access to that knowledge base.

So step one, I'm gonna name the agent and then I'm going to describe what the agent is for so that anyone you know, can understand.

And then once I've set that up, I'm going to select the model that I want to use for that agent and I'm gonna provide specific instructions to the agent as to its purpose.

Now, once I've done that, I'm going to break the action into two steps. One is finding the travel booking and the second is to actually make the travel booking. So I've broken that action down into two, then I'm gonna give the agent access to the knowledge base.

Once I've given that access, I get the review page where I'm able to see the action and see the knowledge base that's been handed to it. And then I'm just gonna create it and that's pretty much it.

So like you saw there was that simple chatbot experience where you were able to pull in information from your repository from a ps and you were able to make those invocations as needed. You are able to do all of that without having to write any code just by pointing the agent to the right knowledge base to the right, right repository and that's it.

So you know, as you can imagine, like there is so many possibilities here that you can address in your own organizations, right? And we can't wait for you to build all these cool new innovations and solve all these complex challenges that you may be facing on your day to day, you know.

Yeah. Now with agents and knowledge base, as I said, like you're closer to deploying that gene application without having to build any of that orchestration layer.

Now this is the sample architecture which is similar to what you saw a little earlier in the demo. So you start off by providing a prompt to your foundation model. That's the first step in that journey.

Once you do that, basically what's happening behind the scenes is the agent is pointing the model to the right knowledge sources. It's then retrieving data from some of the other APIs that were provided to it."

The agent is then collating all of that information and then handing it off to the foundation model for the foundation model to then generate that response. And then once the foundation model has generated response that gets rendered over to the user.

Now it's worth noting here that this could be either a single iteration or you could have multiple iterations. All of those can be handled automatically by the agent based on configurations that you would make.

Now you may be wondering like how do i figure out when to use rag versus fine tune it. Right. So here's a simple way to think about it. As you go from left to right on this graph, you are able to maximize the degree of customization. While you know, on the flip side, what you'll find is that the complexity and the cost along that same axis is going to keep going up.

So at the end of the day, it's gonna be up to you to decide where along this journey from left to right. Are you able to maximize customization for your organization while keeping those complexity and costs in check so that it meets your needs?

On the left, we start with prompt engineering, right? Which is basically simple techniques that you probably are familiar with um on how you can provide instructions to the foundation model, right? And you would do this if let's say the out of the box experience that you're getting with simple prompts are not giving you the kind of return that you wanted.

So a good example, a very simple example here is let's say if you're getting huge responses, but you want like very short and concise responses, you would craft your prompt in a way to say a foundation model like i want you to keep your reply under five sentences. And once you do that, you know it, it basically would understand and apply. There's so much more customizations that you could do through prompting.

Now, if that's not enough. That's when you would go to the next step, which is r, right, which we just talked about a little earlier, you know, linking the knowledge repository to the model so that it gives you better responses.

Now, let's say even after that, you're not able to get the responses that you want, that's when you would tap into fine tuning, right? And you would take annotated data that is available to you, you would provide it to the model and you would change it fundamentally in a way that is more customized to your use case.

Now, let's talk about one of the most common questions that most customers have regarding gender right, security and privacy. Bedrock uses a bunch of techniques to address these concerns.

So first and foremost, you can use aws private link with amazon bedrock to establish a private connectivity between the foundation model and your on premises network without exposing your traffic to the internet.

Second, your data is always encrypted in transit and at rest and you can also use your own keys to encrypt the data. If you use aws key management service or aws cms, you can use that with bedrock and you can create own and manage those encryption keys. So you have full control over how you encrypt the data that is used for, you know, model customizations.

Next, amazon bedrock is compatible with compliance programs like gdpr and hipa and there's so many more coming and then last but not the least. Amazon bedrock like i've mentioned before with amazon bedrock. Your content is not going to be used to improve your the base models as well as it's not shared with any of those third party model providers.

The other key facet that comes up quite often is governance and auditability. Now, with amazon bedrock, we offer a comprehensive monitoring and logging set of capabilities so that you can support governance and any audit requirements. You can use amazon cloud watch to track usage metrics and build customized dashboards that are required for any of your auditing purposes.

Additionally, you can use aws cloud trail to monitor a p activity and troubleshoot any issues as you integrate with, you know, other systems and you integrate these other systems into your generative applications. You can also choose to store the metadata, any requests and responses in amazon s3 buckets. So that's completely up to you.

Now, you know, by now, you should have, you should have had a good understanding of the basics of amazon bedrock as a service.

Now, as you can imagine, the potential of generative a i is is incredibly exciting, right? There's a forecasted seven trillion increase in global gdp that is expected over the next 10 years with generative a i.

Now, let's look at a few common use cases that generative enables you can apply generative a i across pretty much all lines of business including engineering, marketing, customer service, finance and sales. You can use generative a i to improve customer experience through capabilities such as chat bots, virtual agents, intelligent contact centers, personalization and content moderation.

You can boost your employees' productivity with generative a i powered conversational search content creation, text summarization and code generation. among others, you can use generative a i to turbocharge production of all types of creative content from art, music to text images, animations, and videos.

And finally, you can use generative a i to improve business operations with things like intelligent document processing, maintenance assistance, quality control and visual inspection and even synthetic training data generation. The use cases we've reviewed so far can be applied pretty much to all of the industries.

Let's have a quick look at some of these. So you can see financial services, health care, automotive manufacturing, the list goes on right and keep in mind this is not even a comprehensive list of industries or use cases.

Let's take a look at a few customers from some of these industries that are using amazon background. So lonely planet is a premier travel media company that's celebrated for more than 800 travel books. um you know, these offer travel advice and guidance.

So lonely planet has been developing a generative a i solution on aws to help their customers plan epic trips and create life changing experiences with, you know, personalized travel. it by building with clod two on bedrock.

Lonely planet has reduced its itinerary generation costs by nearly 80% lonely planet. Quickly created a scalable and secure a i platform that organizes their contents in minutes to deliver cohesive, highly accurate travel recommendations.

Now they can repackage and repurpose and personalize their content in various ways on their digital platforms and they can do this all based on customer preferences while highlighting trusted local voices.

Similarly, naw group is a leading bank in the uk, serving over 19 million people and supporting communities, families and businesses.

Amazon bedrock allows natwest group to leverage the latest generative models in a secure and scalable platform which their teams of data scientists, their engineers, their technologists are using to experiment and build new services.

Now, with these tools, nawas group is combating the next generation of threats from financial crime as well as they're allowing the customers and natwest employees access to the information that they need in the format that they want when they need it.

Finally, sales force is extending their bring your own a i integration to amazon bedrock for generative a i. You can access sales force data cloud securely and easily with zero etl and use that company data to quickly and securely customize your choice of foundation models using bed.

These customized foundation models are tailored for your company and then can be easily invoked from salesforce data cloud to be used across salesforce.

Now, as you can see, there's so much going on with generative e i and these are just like a few examples.

Now, i'm gonna hand it back over to sherry. She's gonna take you through the final stretch of this session.

Thanks doctor. All right. So just now custom men that generative a i can be applied to a lot of use cases. So identifying the right use case that would benefit your organization and innovate your business is usually a good start for your generative a i journey on aws cloud to help you explore and select the most relevant use case.

We have created the a i use case explorer. It is an easy to use search tool that will help you find the right use case based on your industry business function and the desired business outcome. This tool allows you to explore curated list of a i use cases for your organization and to discover how organizations around the world are using a i to drive business outcomes.

It also allows you to follow our expert curated action plan to realize the power of a i after you select the right use case.

Next step is to empower your developers of all skill levels through a variety of training opportunities. Aws skill builder is a border library that provides more than 600 digital courses. In addition, we also have programs like aws academy, aws restart and aws educate, to empower your team.

We now offer a collection of free and a low-cost trainings to help people understand, implement and begin using generative a i. For example, we recently released a training course on coursera called the generative a i with large language models. This course was developed with a i experts and a i educators like dr andrew ying from deep learning a i.

This is a great opportunity to learn about the large language models and get hands on experience in training, fine tuning and deploying large language models for real world applications.

Next. Consider working towards a proof of concept with help from aws experts. These aws experts guide customers through their generative a i journey on solving diverse business problems, aligning business and the technical stakeholders and building an executable road map for you.

In order to help customers successfully build and deploy generative a i solutions. We establish the aws generative a i innovation center. It is a 100 million investment in a new program that connects aws a iml experts with customers and partners worldwide to accelerate enterprise innovation and success with generative a i.

Lastly, i'd like to share some useful resources for you to get started with amazon bedrock.

Now please grab your phone and take a picture of these three qr codes. The first qr code is amazon bedrock's product page and the second qr code is a step by step tutorial on youtube. The third qr code is amazon bedrock hands-on workshop that this brings us to the end of this presentation and thank you so much for your time and attention.

Yeah, please do complete the survey in in your mobile app and kabe and i are happy to take some questions. Uh