Evaluating A Question Answering Llm Model Using Mlflow Medium

Evaluating Question-Answering (QA) models requires more than checking whether the answer is correct — it also involves measuring exactness, clarity, and safety of responses. MLflow provides a unified API, mlflow.evaluate(), to streamline this process by computing standard metrics, readability scores, and safety checks. This blog explores how to use mlflow.evaluate() for QA tasks, with an example from the finance domain. The mlflow.evaluate() function allows you to evaluate models on a dataset, automatically calculating task-specific metrics and logging results as MLflow artifacts. When model_type="question-answering", MLflow uses built-in evaluators to assess QA performance. Measures how often the model’s prediction matches the reference answer exactly.

In this notebook, we will demonstrate how to evaluate various LLMs and RAG systems with MLflow, leveraging simple metrics such as toxicity, as well as LLM-judged metrics such as relevance, and even custom LLM-judged... We need to set our OpenAI API key, since we will be using GPT-4 for our LLM-judged metrics. In order to set your private key safely, please be sure to either export your key through a command-line terminal for your current instance, or, for a permanent addition to all user-based sessions, configure... Create a test case of inputs that will be passed into the model and ground_truth which will be used to compare against the generated output from the model. Create a simple OpenAI model that asks gpt-4o to answer the question in two sentences. Call mlflow.evaluate() with the model and evaluation dataframe.

MLflow is a powerful open-source platform for managing the machine learning lifecycle. While it’s traditionally used for tracking model experiments, logging parameters, and managing deployments, MLflow has recently introduced support for evaluating Large Language Models (LLMs). In this tutorial, we explore how to use MLflow to evaluate the performance of an LLM—in our case, Google’s Gemini model—on a set of fact-based prompts. We’ll generate responses to fact-based prompts using Gemini and assess their quality using a variety of metrics supported directly by MLflow. For this tutorial, we’ll be using both the OpenAI and Gemini APIs. MLflow’s built-in generative AI evaluation metrics currently rely on OpenAI models (e.g., GPT-4) to act as judges for metrics like answer similarity or faithfulness, so an OpenAI API key is required.

You can obtain: In this step, we define a small evaluation dataset containing factual prompts along with their correct ground truth answers. These prompts span topics such as science, health, web development, and programming. This structured format allows us to objectively compare the Gemini-generated responses against known correct answers using various evaluation metrics in MLflow. This code block defines a helper function gemini_completion() that sends a prompt to the Gemini 1.5 Flash model using the Google Generative AI SDK and returns the generated response as plain text. We then apply this function to each prompt in our evaluation dataset to generate the model’s predictions, storing them in a new “predictions” column.

These predictions will later be evaluated against the ground truth answers When you buy through links on our site, we may earn a commission at no extra cost to you. However, this does not influence our evaluations. MLFLOW is a powerful open source platform to manage the life cycle of automatic learning. Although it is traditionally used for monitoring model experiences, parameter journalization and deployment management, MLFlow recently introduced support to assess large language models (LLMS). In this tutorial, we explore how to use MLFLOW to assess the performance of an LLM – In our case, the Gemini model of Google – on a set of prompts based on facts.

We will generate responses to prompts based on facts using Gemini and will assess their quality by using a variety of measures supported directly by MLFLOW. For this tutorial, we will use the OPENAI and Gemini APIs. The assessment metrics generating the integrated AI of MLFLOW are currently based on OPENAI models (for example, GPT-4) to act as judges for metrics such as the similarity of response or loyalty, therefore a... You can get: In this stage, we define a small set of evaluation data containing factual prompts with their correct -ground truth responses. These invites cover subjects such as science, health, web development and programming.

This structured format allows us to objectively compare the responses generated by the gemini-aux known correct responses by using various evaluation measures in MLFLOW. If you’re experimenting with Large Language Models (LLMs) like Google’s Gemini and want reliable, transparent evaluation—this guide is for you. Evaluating LLM outputs can be surprisingly tricky, especially as their capabilities expand and their use cases multiply. How do you know if an LLM is accurate, consistent, or even safe in its responses? And how do you systematically track and compare results across experiments so you can confidently improve your models? That’s where MLflow steps in.

Traditionally known for experiment tracking and model management, MLflow is rapidly evolving into a robust platform for LLM evaluation. The latest enhancements make it easier than ever to benchmark LLMs using standardized, automated metrics—no more cobbling together manual scripts or spreadsheets. In this hands-on tutorial, I’ll walk you through evaluating the Gemini model with MLflow, using a set of fact-based prompts and metrics that matter. By the end, you’ll know not just how to run an LLM evaluation workflow, but why each step matters—and how to use your findings to iterate smarter. You might wonder, “Don’t LLMs just work out of the box?” While today’s models are impressively capable, they’re not infallible. They can hallucinate facts, misunderstand context, or simply give inconsistent answers.

If you’re deploying LLMs in production—for search, chatbots, summarization, or anything mission-critical—evaluation isn’t optional. It’s essential. MLflow’s recent updates add out-of-the-box support for evaluating LLMs—leveraging the strengths of both OpenAI’s robust metrics and Gemini’s powerful generation capabilities. (Evaluating LLM model with MLFlow and visualizing and tracking results with DagsHub) This project demonstrates how to use OpenAI's language models to create a question-answering system, with the integration of MLflow for experiment tracking and DagsHub for dashboarding the metrics live. The goal is to leverage state-of-the-art NLP models and robust tracking tools to create, manage, and monitor machine learning experiments effectively.

This project uses OpenAI's GPT models to build a question-answering system. By integrating MLflow, we can track the performance and parameters of different model runs. Additionally, by connecting MLflow to DagsHub, we can visualize these metrics in real-time on a dashboard, providing a comprehensive view of our experiments. The architecture of this project is designed to be modular and scalable: Start MLflow server (if not using a remote server): With the emerging of ChatGPT, LLMs have shown its power of text generation in various fields, such as question answering, translating and text summarization.

Evaluating LLMs' performance is slightly different from traditional ML models, as very often there is no single ground truth to compare against. MLflow provides an API mlflow.evaluate() to help evaluate your LLMs. MLflow's LLM evaluation functionality consists of 3 main components: If you're interested in thorough use-case oriented guides that showcase the simplicity and power of MLflow's evaluate functionality for LLMs, please navigate to the notebook collection below: Below is a simple example that gives an quick overview of how MLflow LLM evaluation works. The example builds a simple question-answering model by wrapping "openai/gpt-4" with custom prompt.

You can paste it to your IPython or local editor and execute it, and install missing dependencies as prompted. Running the code requires OpenAI API key, if you don't have an OpenAI key, you can set it up by following the OpenAI guide. There are two types of LLM evaluation metrics in MLflow:

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