BenWilson2
commented
5 months ago Great topic selection! Let's be sure to cover:
- Built-in metrics that are defined by specifying a particular constant in the
model_typeargument ofevaluate() - Defining
extra_metricsto include additional pre-built metrics in addition to the default metrics that are defined by settingmodel_type - Building a custom metric via
make_genai_metric()(and using it) - Building a fully customized metric via
make_genai_metric_with_prompt()(and using it)
@iRahulPandey please let me know how you would prefer to submit working drafts (either through Google Docs or via a Draft PR in .md format) and I'll be happy to review progress and help you work toward getting it published in the blog!
iRahulPandey
Summary
This template is intended to capture a few base requirements that are needed to be met prior to filing a PR that contains a new blog post submission.
Please fill out this form in its entirety so that an MLflow maintainer can review and work with you in the process of drafting your blog content and in reviewing your blog submission PR.
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Acknowledgements
[X]
ack/guideI have read through the contributing guide[X]
ack/readmeI have configured my local development environment so that I can build a local instance of the MLflow website by following the development guide[X]
ack/legalI have verified that there are no legal considerations associated with the nature of the blog post, its content, or references to organizations, ideas, or individuals contained within my post. If I mention a particular organization, idea, or person, I will provide evidence of consent to post by any organization or individual that is mentioned prior to filing my PR.Proposed Title
Evaluate LLMs with custom metrics with LLM as a judge
Abstract
This blog post explores the capability of using large language models (LLMs) as automated judges to evaluate the quality of outputs from retrieval-augmented generation (RAG) pipelines within the MLflow framework. RAG pipelines combine information retrieval with language models to generate outputs informed by relevant textual sources. The post discusses how MLflow's mlflow.evaluate() function can leverage LLMs to score RAG outputs across multiple dimensions like relevance, coherence, and factuality, and even custom metrics, providing an automated way to assess both the retrieved information and the generated text.
Blog Type
blog/how-to: A how-to guide to using core MLflow functionality, focused on a common use case user journeyblog/deep-dive: An in-depth guide that covers a specific feature in MLflowblog/use-case: A comprehensive overview of a real-world project that leverages MLflowblog/best-practices: A comprehensive tutorial that covers usage patterns of MLflow, focusing on an MLOps journeyblog/tips: A short blog covering tips and tricks for using MLflow APIs or the MLflow UI componentsblog/features: A feature-focused announcement that introduces a significant new feature that is recently or not-yet releasedblog/meetup: A report on an MLflow community event or other Linux Foundation MLflow Ambassador Program eventblog/news: Summaries of significant mentions of MLflow or major initiatives for the MLflow projectTopics Covered in Blog
topic/genai: Highlights MLflow's use in training, tuning, or deploying GenAI applicationstopic/tracking: Covering the use of Model Tracking APIs and integrated Model Flavorstopic/deployment: Featuring topics related to the deployment of MLflow models and the MLflow Model Registrytopic/training: Concerned with the development loop of training and tuning models using MLflow for trackingtopic/mlflow-service: Topics related to the deployment of the MLflow Tracking Service or the MLflow Deployments Servertopic/core: Topics covering core MLflow APIs and related featurestopic/advanced: Featuring guides on Custom Model Development or usage of the plugin architecture of MLflowtopic/ui: Covering features of the MLflow UItopic/other: < please fill in >Thank you for your proposal! An MLflow Maintainer will reach out to you with next steps!