Foundation models (FMs) are large-scale AI models trained on extensive and diverse datasets that can be adapted to a wide range of downstream tasks. It is revolutionary and has the most impact in almost any workplace. Even data scientists use foundational model as the starting point for new applications.
These models use architectures like transformers and can perform a broad range of general tasks, such as language understanding, software code generation, text and image generation.
Key traits of foundation models:
Pretrained at scale: These are trained on vast datasets using powerful compute resources. For example, OpenAI trained GPT-4 using 170 trillion parameters and 45 GB training dataset. Another model, BERT, is trained using 340 million parameters and 16GB dataset.
General-purpose: Instead of being built for a single task, these can be fine-tuned or prompted for many applications (e.g., chatbots, summarization, code generation, image recognition).
Adaptable: Organizations can customize these with their own data to serve domain-specific needs. This is a significant difference compared to traditional ML solutions that are specific to the task like text classification.
Importance of Foundational Models:
FMs have already dramatically changed the ML landscape. FMs are used as the base for numerous types of applications. It has reasoning capabilities and can be used to automate tasks. Thus FMs can be used to content generation, integration within workplace for finding answers, chatbot development, clinical diagnosis analysis.
Challenges with Foundation Models:
- Infrastructure: Building foundation model from scratch is expensive and required massive infrastructure.
- Output Reliability: The output might not be reliable and sometimes contain inappropriate or incorrect answers. It might also not have access to the latest development and therefore answer based on past information.
- Bias: The training data can contain bias as in data doesn’t represent reality or algorithmic bias that favours certain outcomes.
AWS Offering – Amazon Bedrock
AWS provides access to foundation models through Amazon Bedrock, a fully managed service that lets you build and scale generative AI applications without managing infrastructure.
This lets you access models from multiple providers (e.g., Anthropic, Meta, AI21 Labs, Stability AI, Amazon’s own Titan models). You don’t need to train or maintain the underlying models yourself.
You can integrate models via an API, customize them with your data, and deploy securely within the AWS ecosystem.
In short: Foundation models are the backbone of modern AI, and AWS makes them accessible and customizable via Amazon Bedrock.
