Optimize LLMOps for Better Outcomes
LLMs and Generative AI
Large Language Models (LLMs) use deep learning algorithms to analyze massive amounts of language data and generate natural, coherent, and contextually appropriate text. Unlike predictive models, LLMs are trained using vast amounts of structured and unstructured data and parameters to generate desired outputs. LLMs are increasingly used in a variety of applications, including virtual assistants, content generation, code building, and more.
Generative AI is the category of artificial intelligence algorithms and models, including LLMs and foundation models, that can generate new content based on a set of structured and unstructured input data or parameters, including images, music, text, code, and more. Generative AI models typically use deep learning techniques to learn patterns and relationships in the input data in order to create new outputs to meet the desired criteria.
Fiddler AI Observability for Generative AI and LLMs
Fiddler has two offerings available for organizations building their LLM strategy:
Fiddler Auditor for Robustness Validation
Evaluate the robustness of LLMs and NLP models
Identify and mitigate weaknesses to improve performance
Ensure your AI solutions are safe, reliable, and more accessible
LLM-based Embeddings’ Monitoring and Performance Analysis
Get early warnings on performance of embeddings
Continuously detect dips in performance caused by data drift
Pinpoint performance issues for deeper analysis
Compare and analyze embedding data
Diagnose and find the root cause of drift
Deep dive and gain context on LLMs’ outputs
What is LLMOps and Why is AI Monitoring Important?
Large Language Model operations (LLMOps) provides a standardized end-to-end workflow for training, tuning, deploying, and monitoring LLMs (open source or proprietary) to accelerate the deployment of generative AI models and applications.
The key to keeping tabs on generative AI models and applications is to continuously monitor them, and resolve data drift and other issues that hinder their ability to generate correct and safe outcomes.