The Biggest Source of Friction in Developing ML Models That Most Data Scientists Overlook

In my experience, most ML projects lack a dedicated experimentation management/tracking system.

As the name suggests, this helps us track:

• Model configuration → critical for reproducibility.

• Model performance → essential in comparing different models.

…across all experiments.

Yet, most ML projects typically leverage manual systems, wherein, they track limited details like:

• Final performance (ignoring the epoch-wise convergence)

• Hyperparameters, etc.

But across experiments, so many things can vary, such as model config, code, data, model type, etc.

Accurately tracking every little detail can be quite tedious and time-consuming.

Nonetheless, the ability to trace the best model to its exact configuration is crucial for several reasons, the primary reason being reproducibility.

While the motivation is quite clear, this is a critical skill that most data scientists and machine learning engineers ignore, and they continue to leverage highly inefficient and manual tracking systems — Sheets, Docs, etc.

To help you develop this critical skill, this is precisely what we are discussing in the latest machine learning deep dive: How To (Immensely) Optimize Your Machine Learning Development and Operations with MLflow.

MLflow provides plenty of functionalities that help machine learning teams effortlessly manage the end-to-end ML project lifecycle.

Being end-to-end means it includes everything we need to:

• Track experimentations

• Share code/model/data

• Reproduce results

• Deploy models

• Register models,

• Create standardized projects and more.

MLflow has four core components, which we discuss in the article:

1. MLflow Tracking for tracking experiments (code, data, model config, and results) and comparing them for model selection.

2. MLflow Projects for packaging code used in data science projects in a format that makes them reproducible on any platform.

3. MLflow Models for deploying machine learning models built in various libraries to diverse serving environments.

4. MLflow Models Registry for creating a dedicated system to manage, organize, version, and track ML models and their associated metadata.

👉 Interested folks can read it here: How To (Immensely) Optimize Your Machine Learning Development and Operations with MLflow.

Adding MLflow to your skill set is one of the easiest ways to improve your Data Science career in 2024.

👉 If you liked this post, don’t forget to leave a like ❤️. It helps more people discover this newsletter on Substack and tells me that you appreciate reading these daily insights.

The button is located towards the bottom of this email.

Thanks for reading!