Mastering Llm Evaluation With Mlflow A Step By Step Guide Using Google

MLflow is a versatile open-source platform designed to manage the full machine learning lifecycle. Traditionally, it has been used for tracking experiments, logging parameters, and managing deployments. Recently, MLflow expanded its capabilities to include evaluation support for Large Language Models (LLMs). This tutorial demonstrates evaluating the Google Gemini model’s performance on fact-based prompts using MLflow. The process also involves OpenAI’s API since MLflow uses GPT-based models for judging metrics like answer similarity and faithfulness. You need API keys from both OpenAI and Google Gemini:

Set environment variables for the API keys securely: Create a dataset with factual prompts and their correct answers. This dataset will be used to compare Gemini’s outputs against known truths. 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 The notebooks listed below contain step-by-step tutorials on how to use MLflow to evaluate LLMs. The first set of notebooks is centered around evaluating an LLM for question-answering with a prompt engineering approach.

The second set is centered around evaluating a RAG system. All the notebooks will demonstrate how to use MLflow's builtin metrics such as token_count and toxicity as well as LLM-judged intelligent metrics such as answer_relevance. Learn how to evaluate various LLMs and RAG systems with MLflow, leveraging simple metrics such as toxicity, as well as LLM-judged metrics as relevance, and even custom LLM-judged metrics such as professionalism. Learn how to evaluate various Open-Source LLMs available in Hugging Face, leveraging MLflow's built-in LLM metrics and experiment tracking to manage models and evaluation results. 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.

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.

Managing large language model experiments without proper tracking leads to lost insights and deployment chaos. MLflow integration provides a structured approach to track LLM experiments, version models, and maintain reproducible AI workflows. This guide shows you how to implement MLflow for LLM experiment tracking and model versioning with practical code examples and deployment strategies. Large language model development involves multiple iterations with different prompts, parameters, and datasets. Without systematic tracking, teams lose valuable experiment data and struggle to reproduce successful results. MLflow solves these challenges by providing:

Traditional model tracking tools fail with LLMs because they don't handle: 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. This project is focused on monitoring and evaluating Large Language Models (LLMs) using MLflow. It demonstrates two key scenarios: This project uses MLflow for tracking, monitoring, and evaluating the performance of LLMs. MLflow's evaluation framework provides a comprehensive set of metrics for assessing model performance. We can use it for both rag based applications and normal LLM based applications.

Before running the code, install the dependencies listed in the requirements.txt file: Make sure to add your OpenAI API keys and other required environment variables to a .env file in the myenv directory: This project supports two main evaluation scenarios:

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