GENERATIVE AI ON AWS

BEST PRACTICES FOR GENERATIVE AI APPLICATIONS ON AWS

The following is a high-level overview of best practices for leveraging Generative AI (Gen AI) on AWS to ensure optimal performance, cost efficiency, and ethical implementation may be summarized as follows:

  • Use Amazon Bedrock and SageMaker to access or fine-tune Gen AI models.
  • Optimize performance and costs with Spot Instances, GPU-based EC2 instances, and AWS Inferentia.
  • Monitor performance using CloudWatch and SageMaker tools.
  • Prioritize ethical AI practices using SageMaker Clarify for fairness and transparency.
  • Secure data using encryption, IAM, and compliance-ready tools.

    • We have a team of AI/ML Engineers, Data Scientists, Software Developers, Solutions Architects, AWS Specialist, and Generative AI Developers to design, implement and maintain Generative AI applications on AWS.

    LEARN MORE

    AMAZON Q

    Amazon Q is a generative AI assistant that transforms how work gets done in your organization. Amazon Q Business can:
  • Answer questions, provide summaries, generate content, and securely complete tasks based on the data in your enterprise systems.
  • Supports the general use case of using generative AI to start making the most of the information in your enterprise.
  • Provides responses in a manner appropriate to your team’s needs.
  • You can create lightweight, purpose-built Amazon Q Apps within your Amazon Q Business Pro subscription.

    • Amazon Q is a generative AI assistant that transforms how work gets done in your organization. With Amazon Q Developer, you can:
  • Understand, build, extend, and operate AWS applications.
  • Supported use cases include tasks that range from coding, testing, and upgrading applications, to diagnosing errors, performing security scanning and fixes, and optimizing AWS resources.
  • The advanced, multistep planning and reasoning capabilities in Amazon Q Developer are aimed at reducing the work involved in common tasks (such as performing Java version upgrades). These capabilities can also help implement new features generated from developer requests.
  • It is also available as a feature in several other AWS services including AWS Chatbot, Amazon CodeCatalyst, Amazon EC2, AWS Glue, and VPC Reachability Analyzer.

    • LEARN MORE

    AMAZON BEDROCK

    Amazon Bedrock is a fully managed service offering high-performing foundation models (FMs) through a single API.

  • Model Options: Includes models from AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, Amazon, and more (e.g., Luma and poolside coming soon).
  • Generative AI Features: Provides tools to build generative AI applications with a focus on security, privacy, and responsible AI practices.
  • Customization: Enables private customization of models using fine-tuning and Retrieval Augmented Generation (RAG).
  • Agent Building: Supports the creation of agents to perform tasks using enterprise systems and data sources.
  • Serverless: No infrastructure management is required.
  • AWS Integration: Seamlessly integrates with AWS services for secure deployment and implementation of generative AI capabilities.
  • Experimentation and Evaluation: Simplifies experimentation with and evaluation of foundation models for specific use cases.
    • LEARN MORE

    AMAZON SAGEMAKER AI

    Amazon SageMaker AI is a fully managed service that provides a complete set of tools for high-performance, low-cost ML for any use case.

  • Integrated Development Environment (IDE): Build, train, and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, and MLOps.
  • Governance Support: Simplified access control, transparency, and auditability for ML projects.
  • Foundation Models (FMs): Build, fine-tune, experiment, retrain, and deploy large models trained on massive datasets with purpose-built tools.
  • Pre-trained Models: Access hundreds of pre-trained models, including publicly available FMs, deployable with a few clicks.
  • User Choice: Provides tools for both data scientists (IDEs) and business analysts (no-code interfaces).
  • Scalable Infrastructure: Fully managed, high-performance, and cost-effective infrastructure to build ML models, including generative AI applications.
  • Repeatable and Responsible Workflows: Automates and standardizes MLOps practices to ensure transparency and auditability.
  • Human-in-the-Loop Capabilities: Improve accuracy and relevance of FMs using human feedback across the ML lifecycle.
  • End-to-End ML Support: Assists in data preparation, model training, and deployment.
  • Amazon Q Developer: Offers code suggestions, answers questions, and helps troubleshoot errors throughout the ML journey.
    • LEARN MORE