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MLtraq: Track your AI experiments at hyperspeed

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 it be nice to load and start tracking in nearly zero time? 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.

The talk will be inspiring and useful for anyone interested in AI/ML experimentation and portable, safe serialization of Python objects.