# 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](/content/demo/index.html) [Documentation](https://documentation.picsellia.com/docs/experiment)

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

```python
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

```python
experiment.store("model.pt")
```

#### Dataset Attachment

Link training data for reproducibility

```python
experiment.attach_dataset(dataset_version)
```

#### Model Export

Push to model registry with one call

```python
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
| Name   | mAP  | Loss | LR    | Epochs | Status    |
|--------|------|------|-------|--------|-----------|
| exp-001| 0.87 | 0.09 | 1e-4  | 100    | completed |
| exp-002| 0.82 | 0.12 | 1e-3  | 80     | completed |
| exp-003| 0.91 | 0.07 | 5e-5  | 150    | running   |

## 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.

```terminal
$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.

[Start Free Trial](/content/trial/index.html) [Request Demo](/content/demo/index.html)