Technology evolves at such a speed that even around the most groundbreaking ideas, innovations appear – and that’s what has just happened with Amazon Bedrock: the generative AI platform of Amazon Web Services (AWS).
Amazon Bedrock is a managed service that makes the foundational models (FMs) of the main AI startups available to us through an API. As a reminder, FMs are very large machine learning models, previously trained with large amounts of data.
Bedrock has different choices of language models or LLMs and embeddings. This allows developers to choose, from a variety of FMs, the one that best suits their needs.
Being serverless, scalability is solved. There is no need to pre-provision servers or worry about oversizing or undersizing the infrastructure.
Models available in Amazon Bedrock
We can quickly start experimenting with different models, customize them with our data, and integrate and deploy them into applications using AWS tools and capabilities.
Depending on the use case, we have a wide range of models available:
It is a versatile model that specializes in summarization, text generation and classification, open-ended Q&A, information extraction, embeddings and search.
Translates text entries (words, phrases or large text units) into numerical representations (embeddings) that contain the semantic meaning of the text.
Follows instructions for a variety of linguistic tasks, such as answering questions, summarizing, or generating text.
Designed for reflective dialogue, content creation, advanced thinking, creativity and coding.
Ideal for generating text in business applications. Offers embeddings for searching, grouping or sorting in over 100 languages.
- Stable Diffusion by Stability.ai
Specializes in generating unique, realistic, high-quality images, artwork, logos and designs.
Fine-Tuning: model personalization
One of the key features of Amazon Bedrock is the ability to fine-tune.
While models are pre-trained with large amounts of information, fine-tuning allows them to be customized for specific tasks or domains, improving their performance and accuracy.
This process also saves time and resources, since fine-tuning a trained model requires less data and computational power than training one from scratch. Finally, this technique helps mitigate errors if performed on a well-curated dataset, which helps generate more representative answers.
Thus, organizations customize FMs with their own data, ensuring that IAG solutions are aligned with their unique needs and specifications.
Bedrock allows customers to use their systems without the information entered leaking into the larger data set used to train the models. Amazon stated that all data is encrypted and remains within the customer’s Virtual Private Cloud (VPC).
Use case: Intelligent chatbots
A company looking to improve interaction with its customers could use Bedrock to develop an intelligent customer service chatbot.
Using Claude 2, it could perform thoughtful dialogues and provide accurate and contextually relevant responses. In addition, with fine-tuning, the chatbot could be trained with industry-specific data: responses will be aligned with consumer expectations and needs.
In conclusion, Amazon Bedrock is a powerful and versatile tool that redefines possibilities in the IAG world. With a variety of advanced models and the ability to customize, it provides what is needed for companies to develop their own AI solutions.
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