Enable Full Reproducibility in ML Model Building

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.

To obtain full reproducibility, you must know proper tools like:

• MLflow, which we discussed in detail here: Optimize Your Machine Learning Development and Operations with MLflow.

• DVC, which we discussed in detail here: Streamline Your Machine Learning Workflow With DVC.

Why care?

Across experiments, so many things can vary like hyperparameters, data, model type, etc.

Accurately tracking every little detail can be quite tedious and time-consuming. What’s more, consider that our ML pipeline has three steps:

If we only made some changes in model training (step 3), say, we changed a hyperparameter, does it make any sense to rerun the first two steps?

No, right?

Yet, typically, most ML pipelines rerun the entire pipeline, wasting compute resources and time.

Of course, we may set some manual flags to avoid this.

But being manual, it will always be prone to mistakes.

To avoid this hassle and unnecessary friction, an ideal tracking system must be aware of the following:

• All changes made to an ML pipeline.

• The steps it can avoid rerunning.

• The only steps it must execute to generate the final results.

We need proper tools to obtain full reproducibility of ML projects, and we have covered them here in detail:

• MLflow: Optimize Your Machine Learning Development and Operations with MLflow.

• DVC: Streamline Your Machine Learning Workflow With DVC.

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

If you care about building and shipping reliable ML projects, this is a must-know skill.

Are you overwhelmed with the amount of information in ML/DS?

Every week, I publish no-fluff deep dives on topics that truly matter to your skills for ML/DS roles.

For instance:

• A Crash Course of Model Calibration – Part 1

• Conformal Predictions: Build Confidence in Your ML Model’s Predictions

• Quantization: Optimize ML Models to Run Them on Tiny Hardware

• A Crash Course on Causality – Part 1

• A Crash Course on Causality – Part 2

• A Beginner-friendly Introduction to Kolmogorov Arnold Networks (KANs)

• 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• A Beginner-Friendly Guide to Multi-GPU Model Training

• 8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science

• Implementing Parallelized CUDA Programs From Scratch Using CUDA Programming

• You Are Probably Building Inconsistent Classification Models Without Even Realizing

• And many many more.

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