Code-Based Logging in MLflow: Enhancing Model Management

Summary

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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

[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

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mlflow / mlflow-website