Ormoda eliminates the friction between data science and production deployment. Test thousands of parameters automatically, deploy models without DevOps, and maintain perfect traceability.
Define parameters once. Test thousands of combinations automatically. Get comprehensive reports in minutes.
LLM-powered parameter tuning discovers optimal configurations you'd never find manually.
Trace any production event back to the exact model version and dataset that created it.
Deploy to production without waiting for DevOps. We handle the infrastructure complexity.
Explore data across three dimensions plus time. Spot patterns invisible in traditional charts.
Connect to any data source. Deploy anywhere. From S3 to real-time streams, we've got you covered.
Eliminate the tedious cycle of manual parameter adjustment. Simply define your model parameters, and Ormoda automatically calculates all permutations, executes tests, and delivers comprehensive reports.
Our intelligent automation ensures thorough exploration of your model's behavior across different scenarios, helping you identify optimal configurations with minimal effort.
# Define your parameter space
params = {
'learning_rate': [0.01, 0.1, 0.3],
'max_depth': [3, 5, 7, 10],
'n_estimators': [100, 200, 500]
}
# Ormoda handles the rest
results = catapult.test_parameters(
model=my_model,
params=params,
dataset=training_data
)
# Get instant insights
best_config = results.get_optimal()
print(f"Best parameters: {best_config}")Our LLM-powered optimization takes parameter tuning to the next level. Simply define your target objectives, and our AI intelligently adjusts and tests parameters until optimal results are achieved.
The system provides detailed reasoning for each adjustment, creating a transparent audit trail. You maintain full control while leveraging AI to discover non-intuitive parameter combinations.
# Define your optimization goal
optimizer = catapult.ai_optimizer(
objective="maximize accuracy while minimizing latency",
constraints={
'inference_time': '< 100ms',
'memory_usage': '< 2GB'
}
)
# AI explores the parameter space intelligently
optimal_model = optimizer.optimize(
base_model=my_model,
validation_data=val_data
)Ensure complete transparency throughout your modeling process. Effortlessly trace back from real-world actions to the precise model version and dataset that initiated them.
When unexpected outcomes occur in production, quickly pinpoint their source. Share in-development models with colleagues, granting visibility into exact versions, inputs, and outputs.
# Every prediction is traceable
prediction = model.predict(new_data)
# Trace back to source
trace = catapult.get_trace(prediction.id)
print(f"Model version: {trace.model_version}")
print(f"Training data: {trace.dataset_hash}")
print(f"Parameters: {trace.parameters}")
# Time travel to any previous state
historical_model = catapult.load_version(
version="v1.2.3",
timestamp="2024-12-01"
)Transform complex data analysis with advanced visualization capabilities. Create sophisticated, interactive visualizations that support 3D representations with time integration—true 4D exploration.
Navigate through three-dimensional space over time to identify patterns and relationships hidden in traditional 2D charts. All with just a few lines of code.
# Create 4D visualization in three lines
viz = catapult.visualize_4d(
data=time_series_data,
x='temperature',
y='pressure',
z='velocity',
time='timestamp'
)
# Interactive exploration
viz.highlight_anomalies(threshold=3.5)
viz.animate(speed=2.0)
viz.export('analysis_results.html')Join leading organizations already accelerating their model deployment with Ormoda