Giter Club home page Giter Club logo

mlflow-website's Introduction

MLflow Website

This repository contains the source code for the MLflow website, which is built using Docusaurus.

Blog Contributions

If you're looking to get a great tutorial, use case, or cover the usage of advanced components within MLflow, head on over to our Contributing Guide to review the blog submission process, authorship guidelines, and helpful tips for developing blog content for MLflow.

Development

Whether you're looking to build a development environment for writing and validating your blog post, or you're a maintainer working on a new page for the website, the Development Guide will help get you started with contributing!

mlflow-website's People

Contributors

abeomor avatar benwilson2 avatar daniellok-db avatar harupy avatar mlflow-automation avatar

Stargazers

avatar avatar avatar avatar avatar avatar

Watchers

avatar avatar avatar avatar avatar

Forkers

benwilson2 daniellok-db minkj1992 abeomor irahulpandey michael-berk awadelrahman

mlflow-website's Issues

[BLOG] - Test validation

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.

PRs that are filed without a linked Blog Post Submission issue and a subsequent agreement on the content and topics covered for the blog post are not guaranteed to be reviewed or merged.

Acknowledgements

  • ack/guide I have read through the contributing guide

  • ack/readme I have configured my local development environment so that I can build a local instance of the MLflow website by following the development guide

  • ack/legal I 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

TEST

Abstract

Testing!

Blog Type

  • blog/how-to: A how-to guide to using core MLflow functionality, focused on a common use case user journey
  • blog/deep-dive: An in-depth guide that covers a specific feature in MLflow
  • blog/use-case: A comprehensive overview of a real-world project that leverages MLflow
  • blog/best-practices: A comprehensive tutorial that covers usage patterns of MLflow, focusing on an MLOps journey
  • blog/tips: A short blog covering tips and tricks for using MLflow APIs or the MLflow UI components
  • blog/features: A feature-focused announcement that introduces a significant new feature that is recently or not-yet released
  • blog/meetup: A report on an MLflow community event or other Linux Foundation MLflow Ambassador Program event
  • blog/news: Summaries of significant mentions of MLflow or major initiatives for the MLflow project

Topics Covered in Blog

  • topic/genai: Highlights MLflow's use in training, tuning, or deploying GenAI applications
  • topic/tracking: Covering the use of Model Tracking APIs and integrated Model Flavors
  • topic/deployment: Featuring topics related to the deployment of MLflow models and the MLflow Model Registry
  • topic/training: Concerned with the development loop of training and tuning models using MLflow for tracking
  • topic/mlflow-service: Topics related to the deployment of the MLflow Tracking Service or the MLflow Deployments Server
  • topic/core: Topics covering core MLflow APIs and related features
  • topic/advanced: Featuring guides on Custom Model Development or usage of the plugin architecture of MLflow
  • topic/ui: Covering features of the MLflow UI
  • topic/other: < please fill in >

Thank you for your proposal! An MLflow Maintainer will reach out to you with next steps!

Evaluate LLMs with custom metrics with LLM as a judge

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.

PRs that are filed without a linked Blog Post Submission issue and a subsequent agreement on the content and topics covered for the blog post are not guaranteed to be reviewed or merged.

Acknowledgements

  • ack/guide I have read through the contributing guide

  • ack/readme I have configured my local development environment so that I can build a local instance of the MLflow website by following the development guide

  • ack/legal I 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 journey
  • blog/deep-dive: An in-depth guide that covers a specific feature in MLflow
  • blog/use-case: A comprehensive overview of a real-world project that leverages MLflow
  • blog/best-practices: A comprehensive tutorial that covers usage patterns of MLflow, focusing on an MLOps journey
  • blog/tips: A short blog covering tips and tricks for using MLflow APIs or the MLflow UI components
  • blog/features: A feature-focused announcement that introduces a significant new feature that is recently or not-yet released
  • blog/meetup: A report on an MLflow community event or other Linux Foundation MLflow Ambassador Program event
  • blog/news: Summaries of significant mentions of MLflow or major initiatives for the MLflow project

Topics Covered in Blog

  • topic/genai: Highlights MLflow's use in training, tuning, or deploying GenAI applications
  • topic/tracking: Covering the use of Model Tracking APIs and integrated Model Flavors
  • topic/deployment: Featuring topics related to the deployment of MLflow models and the MLflow Model Registry
  • topic/training: Concerned with the development loop of training and tuning models using MLflow for tracking
  • topic/mlflow-service: Topics related to the deployment of the MLflow Tracking Service or the MLflow Deployments Server
  • topic/core: Topics covering core MLflow APIs and related features
  • topic/advanced: Featuring guides on Custom Model Development or usage of the plugin architecture of MLflow
  • topic/ui: Covering features of the MLflow UI
  • topic/other: < please fill in >

Thank you for your proposal! An MLflow Maintainer will reach out to you with next steps!

Code-Based Logging in MLflow: Enhancing Model Management

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.

PRs that are filed without a linked Blog Post Submission issue and a subsequent agreement on the content and topics covered for the blog post are not guaranteed to be reviewed or merged.

Acknowledgements

  • ack/guide I have read through the contributing guide

  • ack/readme I have configured my local development environment so that I can build a local instance of the MLflow website by following the development guide

  • ack/legal I 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

Title option1: Code-Based Logging in MLflow: Enhancing Model Management
Title option2: Code-Based Logging in MLflow: An Alternative to Serialization

Abstract

This blog post will explore the concept of code-based logging in MLflow, a feature designed to complement traditional model serialization. I'll discuss the differences between code-based and serialization-based logging, provide heuristics for when to use each approach, and offer examples of code-based logging using pyfunc and LangChain. Although initially developed with LangChain in mind, this feature generalizes well to other use cases with pyfunc.

Blog Type

  • blog/how-to: A how-to guide to using core MLflow functionality, focused on a common use case user journey
  • blog/deep-dive: An in-depth guide that covers a specific feature in MLflow
  • blog/use-case: A comprehensive overview of a real-world project that leverages MLflow
  • blog/best-practices: A comprehensive tutorial that covers usage patterns of MLflow, focusing on an MLOps journey
  • blog/tips: A short blog covering tips and tricks for using MLflow APIs or the MLflow UI components
  • blog/features: A feature-focused announcement that introduces a significant new feature that is recently or not-yet released
  • blog/meetup: A report on an MLflow community event or other Linux Foundation MLflow Ambassador Program event
  • blog/news: Summaries of significant mentions of MLflow or major initiatives for the MLflow project

Topics Covered in Blog

  • topic/genai: Highlights MLflow's use in training, tuning, or deploying GenAI applications
  • topic/tracking: Covering the use of Model Tracking APIs and integrated Model Flavors
  • topic/deployment: Featuring topics related to the deployment of MLflow models and the MLflow Model Registry
  • topic/training: Concerned with the development loop of training and tuning models using MLflow for tracking
  • topic/mlflow-service: Topics related to the deployment of the MLflow Tracking Service or the MLflow Deployments Server
  • topic/core: Topics covering core MLflow APIs and related features
  • topic/advanced: Featuring guides on Custom Model Development or usage of the plugin architecture of MLflow
  • topic/ui: Covering features of the MLflow UI
  • topic/other: Code-based logging with pyfunc

Thank you for your proposal! An MLflow Maintainer will reach out to you with next steps!

Pyfunc in Practice: Creative Applications of MLflow Pyfunc in Machine Learning Projects

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.

PRs that are filed without a linked Blog Post Submission issue and a subsequent agreement on the content and topics covered for the blog post are not guaranteed to be reviewed or merged.

Acknowledgements

  • ack/guide I have read through the contributing guide

  • ack/readme I have configured my local development environment so that I can build a local instance of the MLflow website by following the development guide

  • ack/legal I 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

Pyfunc in Practice: Creative Applications of MLflow Pyfunc in Machine Learning Projects

Abstract

This blog post demonstrates the capabilities of mlflow.pyfunc and how it can be utilized in a machine learning project. The mlflow.pyfunc offers creative freedom and flexibility to the data science team. When utilized correctly, teams can build complex systems encapsulated as a model in mlflow that follows the same model lifecycle as traditional ones. This blog will showcase how to create multimodal setups, seamlessly connect to databases, and implement your own custom fit method using mlflow.pyfunc.

Blog Type

  • blog/how-to: A how-to guide to using core MLflow functionality, focused on a common use case user journey
  • blog/deep-dive: An in-depth guide that covers a specific feature in MLflow
  • blog/use-case: A comprehensive overview of a real-world project that leverages MLflow
  • blog/best-practices: A comprehensive tutorial that covers usage patterns of MLflow, focusing on an MLOps journey
  • blog/tips: A short blog covering tips and tricks for using MLflow APIs or the MLflow UI components
  • blog/features: A feature-focused announcement that introduces a significant new feature that is recently or not-yet released
  • blog/meetup: A report on an MLflow community event or other Linux Foundation MLflow Ambassador Program event
  • blog/news: Summaries of significant mentions of MLflow or major initiatives for the MLflow project

Topics Covered in Blog

  • topic/genai: Highlights MLflow's use in training, tuning, or deploying GenAI applications
  • topic/tracking: Covering the use of Model Tracking APIs and integrated Model Flavors
  • topic/deployment: Featuring topics related to the deployment of MLflow models and the MLflow Model Registry
  • topic/training: Concerned with the development loop of training and tuning models using MLflow for tracking
  • topic/mlflow-service: Topics related to the deployment of the MLflow Tracking Service or the MLflow Deployments Server
  • topic/core: Topics covering core MLflow APIs and related features
  • topic/advanced: Featuring guides on Custom Model Development or usage of the plugin architecture of MLflow
  • topic/ui: Covering features of the MLflow UI
  • topic/other: < please fill in >

Thank you for your proposal! An MLflow Maintainer will reach out to you with next steps!

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.