LLMOps

Q: What is the difference between generative AI and LLM?

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.

Optimize LLMOps for Better Outcomes

Evaluate LLMs for robustness and monitor embeddings for drift

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Fiddler Charts showing a chart for OpenAI embedding model accuracy of .156

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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:

Pre-production

Fiddler Auditor for Robustness Validation

Evaluating OpenAI with Fiddler Auditor. Prompt evaluation with robustness reportwith 1/5 passed.

Evaluate the Robustness of LLMs

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

Safeguard your LLM and NLP models with Fiddler Auditor

Production

LLM-based Embeddings’ Monitoring and Performance Analysis

Fiddler monitoring chart shows OpenAI embeddings drift

Monitor LLM-based Text Embeddings

Get early warnings on performance of embeddings

Continuously detect dips in performance caused by data drift

Pinpoint performance issues for deeper analysis

Watch how to monitor OpenAI embeddings in Fiddler

Fiddler dashboard for LLM monitoring showing OpenAI embedding model accuracy chart

Gain Deep Insights into LLM Behavior

Compare and analyze embedding data

Diagnose and find the root cause of drift

Deep dive and gain context on LLMs’ outputs

Learn how to analyze the performance of LLM-based embeddings in Fiddler

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.

The LLMOps workflow

Monitoring generative AI models