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.
[x] 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
[x] 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
[x] blog/how-to: A how-to guide to using core MLflow functionality, focused on a common use case user journey
[x] 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
[x] topic/tracking: Covering the use of Model Tracking APIs and integrated Model Flavors
[x] 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
[x] topic/other: Code-based logging with pyfunc
Thank you for your proposal! An MLflow Maintainer will reach out to you with next steps!
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
[x]
ack/guide
I have read through the contributing guide[x]
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[x]
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 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
: Code-based logging with pyfuncThank you for your proposal! An MLflow Maintainer will reach out to you with next steps!