Generative AI in action: From prototype to production (AIM276)

Here is a detailed summary of the key takeaways from the video transcription, organized into sections:

Models and Flexibility

  • The need for maximum flexibility to experiment with different models as they are constantly evolving and improving.
  • Options provided by AWS to enable this flexibility:
    • Selecting from a pre-existing set of models on Amazon Bedrock
    • Accessing over 100 foundation models on Bedrock Marketplace
    • Importing and customizing externally trained models into Bedrock
    • Leveraging Amazon SageMaker AI to train models and then importing them into Bedrock

Cost Optimization

  • Cost challenges in transitioning from prototype to production are a major concern for many organizations.
  • AWS offers tools to optimize training and inference costs:
    • Amazon SageMaker HyperPod to efficiently train large models, reducing training time by up to 40%
    • Prompt caching and intelligent prompt routing in Amazon Bedrock to reduce inference costs by up to 90% and 30% respectively
    • Batch inference on select foundation models at 50% of on-demand pricing

Data and Knowledge Bases

  • Data is the key differentiator and lifeblood of production-ready AI systems.
  • Amazon Bedrock Knowledge Bases allow customizing and expanding knowledge bases to provide accurate and tailored responses.
  • Data automation capabilities transform unstructured, multimodal data into usable structured data without coding.
  • Graph RAG and structured data retrieval features enhance the relevance of responses by connecting data sources.

Responsible AI and Governance

  • Responsible AI is a critical driver for successful production deployment, as it builds trust with users.
  • AWS has achieved ISO 42001 certification for AI services, providing global standard-based assurance.
  • Amazon Bedrock Guardrails offer customizable safeguards for safety, privacy, and truthfulness, including automated reasoning checks.
  • Bedrock Agents enable complex, multi-agent workflows with trust at the core.

Unified Environment

  • SageMaker Unified Studio provides a single collaborative environment for the entire AI/ML lifecycle.
  • Integrates data, analytics, and AI capabilities to accelerate the development process.
  • Ensures data privacy and AI governance across the lifecycle.

Customer Examples

  • DoorDash's journey in transitioning their conversational AI assistant for dashers from prototype to production using Bedrock and SageMaker.
  • European Parliament's adoption of Anthropic's Claude model in Bedrock to transform the accessibility of their parliamentary archives.
  • Salesforce's Agentforce platform, built on AWS's unified environment and Atlas Reasoning Engine, to enable trusted and autonomous AI agents.

Your Digital Journey deserves a great story.

Build one with us.

Cookies Icon

These cookies are used to collect information about how you interact with this website and allow us to remember you. We use this information to improve and customize your browsing experience, as well as for analytics.

If you decline, your information won’t be tracked when you visit this website. A single cookie will be used in your browser to remember your preference.

Talk to us