Here is a detailed summary of the key takeaways from the video transcription in markdown format:
Generative AI Adoption and Key Trends
- Customer spending on generative AI grew by 2.5x in less than a year, from $7 million annually to $18 million.
- Customers are increasingly relying on multiple model providers for building generative AI applications.
- Customers are now relying on pre-trained foundation models and customizing their behavior, rather than building their own foundation models.
MLOps, FMOps, and GenOps
- The foundation of operationalizing any AI/ML workloads is governance that spans across AWS services, data, models, and applications.
- MLOps builds on top of governance and brings practices across people, process, and technology to help build scalable, repeatable, and reliable workloads.
- FMOps builds on top of MLOps and brings unique aspects such as selecting and evaluating foundation models, using prompts to modify and manage model behavior, and implementing safeguards.
- GenOps builds on top of FMOps and brings unique aspects required for building end-to-end generative solutions, such as capabilities to build agents, augment foundation models with secondary sources of information, and trace and observe generative applications in production.
Key Challenges and Recommendations
- Fine-tuning Foundation Models: Use tools like SageMaker Ground Truth to provide an easy interface for human feedback and integrate it into automated pipelines.
- Experiment and Model Management: Use managed MLflow on SageMaker to track experiments, log metrics, and register models in a centralized model registry.
- Prompt Management: Save prompts as templates and data, and combine them with evaluation results for repeatability and traceability.
- Building Repeatable Workloads: Use SageMaker Pipelines to build end-to-end model development pipelines that incorporate human feedback and other capabilities.
- Evaluation and Monitoring: Incorporate model, data, user feedback, agent, and system metrics to evaluate and monitor generative AI applications.
- Deployment and Governance: Use SageMaker Model Registry to manage and track models across environments and ensure compliance and governance.
- Implementing Safeguards: Use Bedrock Guard Rails or custom-built safeguards with Lambda Guard to filter out unsafe content and restrict model behavior.
- Cost-Effective Deployment: Use techniques like multi-adapter inference endpoints in SageMaker to optimize cost and performance.
Demo Walkthrough
- Demonstrated reliable experiment tracking using MLflow in SageMaker.
- Showed how to create an iterative fine-tuning workflow using SageMaker Pipelines.
- Implemented safeguards by deploying a Lora Guard model in front of the fine-tuned model.
Customer Story: Rocket Mortgage
- Rocket Mortgage's mission is to make home ownership attainable for everyone by leveraging AI and data.
- They have invested $500 million and 5 years to build their proprietary platform, Rocket Logic, which enables end-to-end AI capabilities.
- By adopting a modernized data and ML infrastructure with SageMaker, they were able to reduce development time by 40-60%, scale from a few dozen models to over 200 models in production, and automate 3.7 billion AI and data-driven decisions.
- They have built solutions like Rocket Assist, a generative AI-powered chatbot, and Rocket Navigator, an internal tool to make the latest generative language models accessible to their team members.
Resources
- SageMaker MLOps page
- Generative AI Workshop
- LinkedIn profiles of the presenters