MLOps

Track Experiments Ship Better Models

Full reproducibility with metrics, parameters, and artifacts. Compare runs, collaborate with your team, and version models automatically.

Works with:

+more

See It In Action Documentation

Best Model

mAP: 0.91 • exp-003

Training on GPU

Logging

Log everything, miss nothing

Capture metrics, parameters, artifacts, and evaluations with a simple API. Full lineage tracking with minimal integration code.

Scalar

Single values (loss, accuracy)

Line

Time-series metrics

Image

Visualizations & samples

Table

Structured data

Histogram

Distributions

Confusion Matrix

Classification results

train.py

from picsellia import Client

client = Client()
project = client.get_project("my-project")
experiment = project.get_experiment(
  "my-experiment"
)

# Log hyperparameters
experiment.log_parameters({
  "learning_rate": 1e-4,
  "batch_size": 32
})

# Log metrics during training
for epoch in range(epochs):
  experiment.log(
    "train_loss",
    loss.item()
  )

Artifact Storage

Store checkpoints, configs, and outputs

experiment.store("model.pt")

Dataset Attachment

Link training data for reproducibility

experiment.attach_dataset(dataset_version)

Model Export

Push to model registry with one call

experiment.export_as_model("my-model")

Compare trainings instantly

See the exact training distribution, hyperparameters, and augmentations behind every performance change. Find your best model faster.

Side-by-side metric comparison

Parameter diff highlighting

Dataset version tracking

Collaborative comments

EXPERIMENT COMPARISON

Build training pipelines with ease

Picsellia CV Engine is a modular toolkit for constructing computer vision workflows. Composable steps, framework extensions, and CLI automation.

Training Pipelines

Data → Model → Results. Streamlined training processes with built-in logic.

Processing Pipelines

Dataset transformation, pre-annotation, and data cleaning operations.

Framework Extensions

Pluggable architecture supporting multiple training libraries.

$pip install picsellia-cv-engine

# Initialize a new training pipeline
$pxl-pipeline init --type training

# Run locally for testing
$pxl-pipeline test

# Deploy to Picsellia cloud
$pxl-pipeline deploy --gpus 1

Version your models automatically

Export experiments to the model registry with a single call. Track versions, compare performance, and deploy with confidence.

Automatic version incrementation

Framework metadata (TensorFlow, PyTorch, etc.)

Docker configuration for deployment

Lineage to training experiment

COCO metrics built-in

Add predictions, compare against ground truth, and compute standard evaluation metrics automatically.

• 0.91 mAP@50
• Mean Average Precision
• 0.68 mAP@50:95
• Strict mAP
• 0.89 Precision
• True Positives / Predicted
• 0.87 Recall
• True Positives / Actual

Supports rectangles, polygons, classifications, and keypoints

Ready to track your experiments?

Start logging metrics, comparing runs, and shipping better models with full reproducibility.

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