Amazon Bedrock, which was announced in April 2023, has drawn a lot of attention from businesses looking to leverage their existing AWS architecture for building Generative AI applications. Amazon announced the General Availability (GA) of Bedrock on September 28 — a service that offers a choice of Generative AI models from Amazon and third-party providers through an API-based interface.
With the current market landscape (high demand for leveraging GAI for building innovative business capabilities) and increasing competitive offerings (Azure’s OpenAI partnership & Azure AI and Google Cloud’s Vertex AI and Duet AI), expanding the capabilities of its end-to-end Generative AI landscape has been a key priority for Amazon. Amazon Bedrock is the foundational building block to provide these GAI capabilities along with the existing AWS ecosystem of 30+ AI and ML Services.
While there are many features as part of the offering, this article summarizes key features that help enterprise organizations elevate their AI journey by addressing their novel concerns:
#1 – Unified Serverless Abstraction Layer for Foundation Models
Amazon Bedrock provides a serverless abstraction to the foundation models from leading providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI and Amazon. With AWS’s in-built security, scalability, performance and manageability — it is a key differentiator for existing AWS customers. Note that Bedrock provides provisioned throughput capability to ensure a consistent user experience during peak traffic hours.
AWS provides Boto3 — a Python-based SDK as a unified interface to interface with different foundation models. Refer to the Additional Information section below for code examples.
#2 Seamless Integration with existing AWS-based Cloud Architecture
The key differentiator for Bedrock is the seamless integration with enterprise-specific Cloud architecture, particularly if you are AWS-oriented. In my observation, while Generative AI capabilities are yet to be enabled across AWS services, it will happen incrementally. Currently, the following services support Generative AI-based applications:
- Amazon Sagemaker provides the core capabilities for Machine Learning along with launching foundation models through Sagemaker Jumpstart (see additional information section for more details).
- Integration with Amazon CloudWatch to track usage metrics and build customized dashboards along with AWS CloudTrail to monitor API activity and troubleshoot issues.
- Amazon OpenSearch has launched Vector Search capability in June 2023.
- Amazon QuickSight Q enhances business analysts’ experience by enabling them to ask questions based on their data using natural language queries along with Generative BI dashboard authoring capabilities.
- Amazon Neptune ML is a new capability of Neptune that uses Graph Neural Networks (GNNs).
#3 Agents for Amazon Bedrock and RAG SupportInvoking APIs dynamically to carry out complex business processes is made simple with the aid of Agents for Amazon Bedrock. Key features are:
#4 Amazon CodeWhisperer for integrated Developer’s Experience
- Amazon CodeWhisperer, an AI coding companion built by Amazon, helps developers build software applications faster by providing code suggestions across 15 languages.
- CodeWhisperer’s ability to integrate with the organization’s codebase and generate recommendations accordingly, mitigates the security and related concerns for enterprise applications.
- Amazon CodeWhisperer customization capability elevates enterprise-specific code generation capability as it can include the enterprise’s internal APIs, libraries, best practices and architectural patterns.
Additional Information
A. Amazon Sagemaker Jumpstart provides a unified interface to explore and launch a foundation model.
B. Launch Custom Model with fine-tuned using Enterprise Data
- Launch Custom Models from Amazon Bedrock:
2. Create a fine-tune job with your data sources:
C. An Example of Generating Blog with Bedrock and Amazon & AI21 Labs Models
import boto3
import json
import pandas as pd
bedrock = boto3.client(service_name="bedrock", region_name="us-west-2")
# To see all the FMs
# bedrock.list_foundation_models()
prompt_data = (
"""Write a paragraph about benefits of using AWS Services in 3 Bullet Points"""
)
body = json.dumps(
{
"inputText": prompt_data,
"textGenerationConfig": {"maxTokenCount": 200, "temperature": 0.6, "topP": 1},
}
)
modelId = "amazon.titan-tg1-large"
accept = "application/json"
contentType = "application/json"
response = bedrock.invoke_model(
body=body, modelId=modelId, accept=accept, contentType=contentType
)
response_body = json.loads(response.get("body").read())
titan_response = response_body.get("results")[0].get("outputText")
# Switching the model to AI21 Labs' Jurassic-2 Ultra
modelId = "ai21.j2-ultra-v1"
body = json.dumps(
{
"prompt": prompt_data,
"maxTokens": 200,
"temperature": 0.6,
"topP": 1,
}
)
response = bedrock.invoke_model(
body=body, modelId=modelId, accept=accept, contentType=contentType
)
response_body = json.loads(response.get("body").read())
ultra_response = response_body.get("completions")[0].get("data").get("text")
response_df = [[titan_response, ultra_response]]
pd.set_option("display.max_colwidth", 0)
pd.DataFrame(
response_df, columns=["Amazon Titan Text TG1 Large", "AI21 Labs Jurassic-2 Ultra"]
)D. Current Limitations Observed in Amazon Bedrock
- It has been launched in two regions only — US East (N. Virginia) and US West (Oregon).
- Comparatively, Google Vertex AI has launched Generative AI Search and Conversation Builder . Bedrock has not launched a similar user-friendly capability yet, but you can leverage Amazon Kendra and Amazon Lex.
References
- AWS Announcement of New Tools to Help Every Business Embrace Generative AI
- Amazon Bedrock GA Announcement (Sep 28)
- GitHub Repository for Amazon Bedrock Workshop
- Llama2 on Amazon Bedrock for Research & Commercial Use
- Redis Enterprise Cloud Integration With Amazon Bedrock
- Amazon Bedrock User Guide
