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MLtraq: Track your AI experiments at hyperspeed

PyData: Research & Applications
Terrace 2A
14:00 on 12 July 2024
30 minutes


Every second spent waiting for initializations and obscure delays hindering high-frequency logging, further limited by what you can track, an experiment dies. Wouldn’t loading and starting tracking in nearly zero time be nice? What if we could track more and faster, even handling arbitrarily large, complex Python objects with ease?

In this talk, I will present the results of comparative benchmarks covering Weights & Biases, MLflow, FastTrackML, Neptune, Aim, Comet, and MLtraq. You will learn their strengths and weaknesses, what makes them slow and fast, and what sets MLtraq apart, making it 100x faster and capable of handling tens of thousands of experiments.

This presentation will not only be enlightening for those involved in AI/ML experimentation but will also be invaluable for anyone interested in the efficient and safe serialization of Python objects.

The speaker

Michele Dallachiesa

Michele Dallachiesa

Michele is an independent consultant specializing in de-risking AI projects for companies. He brings over a decade of experience from both public and private sectors, with expertise in robotics, management of megaprojects, agents, LLMs, and more. Connect with him on LinkedIn at