Getting Started With Mlflow For Llm Evaluation Satoshi Source

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: os.environ[“OPENAI_API_KEY”] = getpass(‘Enter OpenAI API Key:’) os.environ[“GOOGLE_API_KEY”] = getpass(‘Enter Google API Key:’) 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. 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: 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. 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

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: This documentation covers MLflow's GenAI evaluation system which uses: Note: This system is separate from the classic ML evaluation system that uses mlflow.evaluate() and EvaluationMetric. The two systems serve different purposes and are not interoperable. MLflow's evaluation and monitoring capabilities help you systematically measure, improve, and maintain the quality of your GenAI applications throughout their lifecycle from development through production.

A core tenet of MLflow's evaluation capabilities is Evaluation-Driven Development. This is an emerging practice to tackle the challenge of building high-quality LLM/Agentic applications. MLflow is an end-to-end platform that is designed to support this practice and help you deploy AI applications with confidence. Before you can evaluate your GenAI application, you need test data. Evaluation Datasets provide a centralized repository for managing test cases, ground truth expectations, and evaluation data at scale. If you're new to MLflow or seeking a refresher on its core functionalities, these quickstart tutorials here are the perfect starting point.

Jump into the tutorial that best suits your needs and get started with MLflow. This tutorial walk through the basic experiment tracking capabilities of MLflow by training a simple scikit-learn model. If you are new to MLflow, this is a great place to start. This tutorial walk through the basic LLMOps and GenAI capabilities of MLflow, such as tracing (observability), evaluation, and prompt management. If you are AI practitioner looking to build production-ready GenAI applications, start here. MLflow's experiment tracking capabilities have a strong synergy with large-scale hyperparameter tuning.

This tutorial guides you through the process of running hyperparameter tuning jobs with MLflow and Optuna, and effectively compare and select the best model. → Getting Started with Hyperparameter Tuning MLflow transforms how software engineers build, evaluate, and deploy GenAI applications. Get complete observability, systematic evaluation, and deployment confidence—all while maintaining the flexibility to use any framework or model provider. MLflow provides a complete platform that supports every stage of GenAI application development. From initial prototyping to production monitoring, these integrated capabilities ensure you can build, test, and deploy with confidence.

Trace every LLM call, prompt interaction, and tool invocation. Debug complex AI workflows with complete visibility into execution paths, token usage, and decision points. Systematically test with LLM judges, human feedback, and custom metrics. Compare versions objectively and catch regressions before they reach production. Serve models with confidence using built-in deployment targets. Monitor production performance and iterate based on real-world usage patterns.

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