AWS and Generative AI: A Synergistic Journey into Innovation
Generative AI stands as a form of artificial intelligence capable of generating novel content and concepts, encompassing conversations, narratives, images, videos, and music. Similar to other AI systems, generative AI relies on extensive machine learning models, commonly known as foundation models (FMs), which are large-scale and pre-trained on vast datasets. The evolution of machine learning, notably the advent of the transformer-based neural network architecture, has given rise to models with billions of parameters or variables. The expansive parameter count in foundation models equips them to comprehend intricate concepts, enabling them to perform a wide range of tasks.
AWS is committed to meeting clients’ expanding and changing generative AI demands. AWS has highlighted four critical aspects for rapidly developing and deploying generative AI systems at scale:
- Simplify the build process effortlessly
- Stand out by leveraging unique dataset
- Boost efficiency and output
- Achieve optimal performance at a cost-effective rate
AWS offers the following services to help you expedite your Gen AI journey:
Amazon Bedrock
“The simplest way to build & scale generative AI applications with FMs.”
Amazon Bedrock is a fully managed service that provides a selection of high-performing foundation models (FMs) from leading AI companies such as AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, as well as a comprehensive set of capabilities required to build generative AI applications with security, privacy, and responsible AI. Users can simply experiment with and assess top FMs for their use case using Amazon Bedrock, privately customise them with their data using techniques like fine-tuning and Retrieval Augmented Generation, and construct agents that perform tasks utilising their business systems and data sources.
AWS Trainium
“Purpose-built accelerators for generative AI”
AWS Trainium is a second-generation machine learning accelerator designed specifically for deep learning training of models with 100B+ parameters. Each EC2 Trn1 instance deploys up to 16 AWS Trainium accelerators to provide a high-performance, low-cost solution for cloud deep learning training. While the usage of deep learning is increasing, many development teams are constrained by set budgets, limiting the scope and frequency of training required to enhance their models and applications. Trainium-based EC2 Trn1 instances address this issue by giving quicker time to train while saving up to 50% over equivalent Amazon EC2 instances.
AWS Inferentia
“Purpose-built accelerators for generative AI”
AWS Inferentia accelerators are intended to provide great performance at the lowest possible cost for deep learning inference applications. Amazon EC2 Inf1 instances powered by the first-generation AWS Inferentia accelerator give up to 2.3x greater throughput and up to 70% lower cost per inference than equivalent Amazon EC2 instances. Many clients have used Inf1 instances and realised the performance and cost benefits, including Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa.
AWS HealthScribe
“A HIPAA-eligible automatic note-generation service for clinical applications”
AWS HealthScribe automatically recognises speaker roles, classifies talks, extracts medical terminology, and creates detailed preliminary clinical transcripts and notes. AWS HealthScribe eliminates the need to integrate and optimise various AI services, allowing users to accelerate deployment. AWS HealthScribe, powered by Amazon Bedrock, enables faster and easier integration of generative AI capabilities without the need to maintain underlying machine learning (ML) infrastructure or train healthcare-specific large language models (LLMs).
Amazon SageMaker JumpStart
“ML hub with FMs, built-in algorithms, and prebuilt ML solutions that can be deploy with just a few clicks”
Amazon SageMaker is a fully managed service that combines a diverse range of tools to allow high-performance, low-cost machine learning (ML) for any application. SageMaker allows users to design, train, and deploy ML models at scale by combining notebooks, debuggers, profilers, pipelines, MLOps, and other tools in a single integrated development environment (IDE). SageMaker meets governance standards by facilitating access control and transparency in ML initiatives. Furthermore, users may create their own FMs, which are huge models trained on massive datasets, using purpose-built tools to fine-tune, experiment, retrain, and deploy FMs. SageMaker provides access to hundreds of pretrained models, including publically accessible FMs, that can be deployed in a matter of seconds.
Generative BI capabilities in Amazon QuickSight
“New FM-powered capabilities for business users to extract insights, collaborate, & visualize data”
Business intelligence (BI) customers can quickly create, find, and share meaningful insights and narratives using natural language experiences in just a few seconds with the help of Amazon Q’s Generative BI capabilities in QuickSight. Users no longer have to wait for BI teams to update dashboards and data in response to new inquiries. Natural language querying, created narratives, and automated contextual summaries are available for self-service by users.
Amazon CodeWhisperer
“Build apps faster and more securely with an AI coding companion”
Using the code that already exists in the IDE and user’s comments, Amazon CodeWhisperer creates code recommendations in real time, ranging from snippets to entire functions. Additionally, it facilitates natural language to bash translation on the command line and CLI completions.
Please feel free to write @ piyush.jalan93@gmail.com for any queries AWS provided Gen AI services & stay tuned for next write-up.
Thank you!
