The blog describes TruEra’s collaboration in co-writing with Josh Reini, Shayak Sen, and Anupam Datta from TruEra. It highlights Amazon SageMaker JumpStart’s provision of pretrained foundation models, outlines the need for adapting foundation models to new tasks or domains, and mentions TruLens’ framework for extensible, automated evaluations. Additionally, it details the processes of deploying and fine-tuning models using SageMaker JumpStart. Furthermore, the blog discusses using TruLens for performance evaluation and refining foundation models for LLM applications. It also elaborates on incorporating TruLens to instrument LLM application call stacks and evaluating for honest, harmless, and helpful responses. Lastly, it introduces the authors and their roles in TruEra.
Amazon SageMaker JumpStart and TruEra for Middle Managers
Accelerating Foundation Model Deployment
Amazon SageMaker JumpStart offers pretrained foundation models such as Llama-2 and Mistal 7B for quick deployment to endpoints. These models perform well with generative tasks like text crafting and image production. However, they may need adaptation for specific tasks or domains.
Adapting Foundation Models
To adapt foundation models, you can fine-tune them using SageMaker JumpStart. Fine-tuning enhances model efficacy and can be measured against a ground truth dataset. TruLens, an open source library, helps with framework for automated evaluations, mitigating the challenge of expensive ground truth datasets.
Practical Evaluation Techniques
TruLens evaluations use feedback functions to verify absence of hallucination, context relevance, and groundedness. These functions are implemented using off-the-shelf models from Amazon Bedrock, ensuring reliable evaluations across development and production.
Deploying and Evaluating Models
SageMaker allows easy deployment of foundation models, while TruLens helps set up evaluations to assess model performance, including context relevance, groundedness, and answer relevance.
Fine-Tuning and Performance Evaluation
Fine-tuning models using SageMaker JumpStart can substantially improve performance metrics and similarity to ground truth test sets, although it may lead to slightly increased latency.
Instrumenting and Monitoring with TruLens
TruLens provides instrumentation and logging, allowing for evaluations and diagnostics at scale. It helps measure app performance dynamically across various metrics even in cases where ground truth is not available.
Practical AI Solutions
By leveraging Amazon SageMaker JumpStart and TruEra, middle managers can accelerate model deployment, fine-tune models, and iterate on LLM applications effectively. Implementing AI solutions gradually and connecting with experts for AI KPI management can further optimize the AI adoption process.
Spotlight on Practical AI Solution
Check out the AI Sales Bot from itinai.com/aisalesbot, designed to automate customer engagement and manage interactions across all customer journey stages.
For more information about AI solutions and continuous insights into leveraging AI, connect with us at hello@itinai.com or stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.
List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Deploy foundation models with Amazon SageMaker, iterate and monitor with TruEra
- AWS Machine Learning Blog
- Twitter – @itinaicom
