AWS Generative AI: How To Get Started With Powerful LLMs for Business
Generative AI exploded on the scene in November, 2022 when OpenAI released ChatGPT to the public. According to OpenAI, ChatGPT acquired 1 million users just 5 days after launching. By comparison, it took Instagram about 2.5 months to reach 1 million downloads. Meanwhile Netflix had to wait around 3.5 years to reach 1 million users.
For the first time in the history of AI we found ourselves in a world where the average person gained access to revolutionary AI tools even before enterprises did. The tables have turned, and now businesses are sprinting, trying to catch up with the AI-savvy public, wanting to harness the same tools that millions already use daily to be more productive.
The pressing need? Equipping businesses with Generative AI tools like ChatGPT, ensuring they're secure, ethical, and controlled, so there’s no accidental leakage of company secrets. Generative AI isn't just about creating art or answering questions; it's reshaping entire industries at warp speed.
“Just like the microchip brought the marginal cost of compute to zero, and the Internet brought the marginal cost of distribution to zero, generative AI promises to bring the marginal cost of creation to zero.”
Amazon Web Services (AWS) is one of the early vendors providing LLMs tailored for enterprise requirements. AWS provides a robust lineup of services to harness generative AI, including Amazon Bedrock, CodeWhisperer, EC2 instances, and more. Let’s dive into how you can leverage Amazon's solutions to integrate generative AI and responsibly transform your business, starting with the basics.
What is Generative AI?
Generative AI is a subset of AI that specializes in creating new, previously non-existent content. This includes diverse forms such as text, images, videos, and music. But what sets it apart from other AI forms is its underlying architecture and the sheer scale of its capabilities.
Foundation Models: The Pillars of Generative AI
Powering Generative AI are Foundation Models (FMs). These are vast machine learning models pre-trained on enormous amounts of data. The recent surge in the capabilities of Generative AI can be attributed to the introduction of transformer-based neural network architectures, which have enabled the creation of models with billions of parameters.
To put this growth into perspective, consider this: In 2019, the largest pre-trained model had 330 million parameters. Fast forward to today, and we have models boasting more than 500 billion parameters. That's an astounding 1,600x increase in just a few years!
Models such as GPT3.5, BLOOM, and the text-to-image model, Stable Diffusion, exemplify the rapid advancements in this field. These models are setting new benchmarks and expanding the horizons of what's possible with Generative AI.
AWS, known for its robust cloud infrastructure, offers a platform for these massive Generative AI models to be accessible to developers, businesses, and researchers. With AWS's scalable infrastructure, deploying and utilizing these models has never been easier.
AWS Generative AI Arsenal
In April 2023, AWS unveiled its suite of generative AI solutions, including Amazon Bedrock, CodeWhisperer, new EC2 instances, and more. This marked a major milestone in making enterprise-grade generative AI accessible to organizations of all sizes. The services provide the robust infrastructure and responsible development tools organizations need to harness the promise of this emerging technology.
While AWS sets the stage with impressive infrastructure, there's a broader narrative at play. It's not just about the tools available, but also about how these tools are wielded.
How to Approach AWS Generative AI
Before diving into AWS Generative AI technology, it's crucial to establish a game plan:
Select the Right Model: AWS boasts an extensive array of models tailored to various needs. From large language models like GPT3.5 to visual marvels like Stable Diffusion, the choice is vast.
Integrate with AWS Services: AWS provides a seamless integration experience. Leverage platforms such as AWS Lambda and S3 to ensure your Generative AI applications scale and deploy with ease.
Experiment and Iterate: The beauty of Generative AI lies in its versatility. Experiment with different models, fine-tune them, and iterate based on feedback. AWS provides an optimal playground for such endeavors.
Having charted the approach, let's dive into what AWS brings to the Generative AI table.
Amazon Bedrock: Access Leading Foundation Models
Amazon Bedrock makes cutting-edge foundation models easily accessible via API so you can find the right model for your use case. Bedrock offers managed access to models, including:
Amazon Titan: Text generation, summarization, QA, search, and more
Anthropic Claude: Thoughtful dialogue and content creation
AI21 Jurassic-2: Multi-lingual instruction-following models
Stability AI Stable Diffusion: High-quality image generation
With Bedrock's serverless experience, you can quickly integrate these models into your applications using familiar AWS tools like SageMaker, Lambda, and Cloud9.
Fine-Tune Models with Your Data Using Amazon SageMaker
While pre-trained models offer great results out-of-the-box, you can customize them to your domain by fine-tuning them with private data using Amazon SageMaker.
Provide SageMaker with labeled examples like past marketing emails or product photos. The model rapidly learns your unique patterns and nuances. This powers more tailored, branded experiences for customers.
SageMaker fully manages the training process with options to deploy models for real-time or batch inferencing. Your sensitive data remains private and secure.
Scale Training & Inference with Amazon EC2
Developing generative AI models requires immense computing power. AWS offers EC2 instance types purpose-built for cost-effective training like Trn1n powered by AWS Trainium chips.
For low-latency inference, when models are deployed in production, new Inf2 instances with AWS Inferentia chips offer up to 40% better price performance than alternatives.
The savings and simplicity of cloud infrastructure lets your team focus on maximizing generative AI's benefits rather than managing servers.
Boost Developer Productivity with Amazon CodeWhisperer
Amazon CodeWhisperer uses a foundation model to provide code suggestions in real time as developers write code. This allows them to build faster while focusing on more creative tasks. In fact, an Amazon productivity challenge found that participants who used CodeWhisperer completed tasks 57% faster, on average, and were 27% more likely to complete them successfully than those who didn’t use CodeWhisperer.
CodeWhisperer integrates directly into IDEs like VSCode and supports languages including Python, JavaScript, Java, and Go. It's trained on public code as well as Amazon's own code bases, so it handles AWS services particularly well.
Individual developers can access CodeWhisperer for free. It includes built-in scanning to flag vulnerabilities and licensing issues in generated code.
Accelerate ML with Amazon JumpStart
For those new to ML, Amazon JumpStart provides an extensive library of sample code, pre-trained models, and tutorials to get started quickly.
Resources are organized by solutions for common use cases like computer vision, forecasting, and natural language processing. JumpStart will help you quickly prototype and operationalize AI applications.
Responsible Generative AI Practices
While generative AI enables breakthrough innovations, it also introduces risks around data privacy, bias, misinformation, and more. Responsible development is critical.
Best practices include human oversight of outputs, transparency, continuous monitoring, and a cross-functional approach. AWS provides guidance and services to ensure generative AI benefits customers and society.
Diving Deeper: Practical Applications of Generative AI on AWS
For those curious about the practical implications of Generative AI, here's a glimpse into how it's revolutionizing various domains by working hand-in-hand with human expertise:
Content Creation: Generative AI assists journalists and content creators by suggesting drafts, researching data, or enhancing marketing materials, making the content creation process more efficient while retaining the human touch.
Art and Design: While artists remain at the heart of creation, Generative AI offers innovative tools and inspirations. It can suggest artwork compositions, music beats, or design layouts, giving artists a broader palette to work from.
Research and Development: In domains like pharmaceuticals, Generative AI augments scientists by suggesting potential molecular structures or highlighting possible paths of research, accelerating drug discovery without compromising precision.
Gaming and Entertainment: Think of video games where AI-generated environments and characters enrich the gaming landscape, but game developers still shape the narrative, ensuring a tailored experience every playthrough.
For a comprehensive understanding of how generative AI can be seamlessly integrated into business workflows, refer to Use Cases for ChatGPT - How to use LLMs For Your Business.
The Future with AWS Generative AI
Generative AI promises to transform every industry by powering new products, workflows, and experiences. AWS delivers cutting-edge infrastructure paired with responsible development tools and guidance to turn that promise into reality, and they say they have a lot more coming.
With pragmatic governance and a focus on business impact, AWS customers can lead their industries into an AI-powered future.
Subscribe to our Weekly Data & AI Newsletter
Stay ahead of the curve with data science & machine learning Insights, resources, and tips
Subscribe to our weekly Data Science & Machine Learning Technology Newsletter
