m:lop
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Experiment Tracking for
Machine Learning

The modern way to track, optimize, and collaborate on machine learning experiments.

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Enterprise Experiment Tracking Platform

mlop is the comprehensive experiment tracking platform that streamlines your machine learning operations, from experiments to deployment.

Model Performance

Track model accuracy and performance metrics over time.

Watch the Model Learn

Track the model's parameters and gradients in real-time.

param/fc.weight

Reproducibility & Git Status

Track experiments, model versions, and uncommitted files in real‑time.

BERT Fine-tuning
git: a2dg3t
ResNet50 v2.1
git: b7h5k1
Uncommitted files
XGBoost Tuning
git: c9j4l0
YOLOv5 v1.3
git: d3m8n2
Uncommitted files

Identify Problems

Stay informed with real-time alerts about your model's performance

Email notifications
Critical issue alerts
Performance monitoring

Seamless Integration

Seamlessly connect with your code base and development workflow.

Integrate in minutes.

100% compatible with Weights & Biases API for easy migration.
setup.py
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import mlop
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# Define your config
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config = {
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  "learning_rate": 0.02,
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  "epochs": 100,
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}
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# Initialize a run
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run = mlop.init(
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  project="vision-language-model",
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  name="gpt4o-vision",
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)
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# Train the model
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for epoch in range(config["epochs"]):
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    # Log metrics
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    run.log({
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        "loss": loss,
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        "accuracy": accuracy,
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        "img": mlop.Image(gen_model)
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    })
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run.finish()