TurboTune Dashboard
XGBoost Model Optimization
Running...Best Score0.9847
Improvement+23%
Complex Setup Required
Lines: 15+ | Setup Time: 2-3 days
# Traditional Optuna approach
import optuna
import joblib
from sklearn.model_selection import cross_val_score
from xgboost import XGBRegressor
def objective(trial):
lr = trial.suggest_float('learning_rate', 1e-5, 1e-1, log=True)
max_depth = trial.suggest_int('max_depth', 1, 10)
n_estimators = trial.suggest_int('n_estimators', 50, 300)
subsample = trial.suggest_float('subsample', 0.5, 1.0)
model = XGBRegressor(
learning_rate=lr,
max_depth=max_depth,
n_estimators=n_estimators,
subsample=subsample
)
cv_scores = cross_val_score(model, X, y, cv=5)
return cv_scores.mean()
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=100)
# 15+ lines just for basic setup...2x Better Performance
Intelligent Bayesian optimization with automatic parameter detection
5 Minute Setup
Add one decorator and you're done. No complex configuration needed
Pay Per Use
Only pay for compute time you actually use. No monthly subscriptions
The Problem
The Hyperparameter Tuning Problem
Stop wasting days on manual tuning and complex setup
Manual Tuning
- Days of trial and error
- Suboptimal results (30-50% performance left on table)
- Wasted compute resources
- Frustration and burnout
Existing Tools
- Complex setup (2-3 days for Optuna)
- Steep learning curves
- Heavy code modifications required
- Infrastructure management overhead
Your Reality
- Tight deadlines
- Limited team resources
- Need results now, not weeks later
- Business pressure for better models
40%
of data scientist time spent on hyperparameter tuning
30-50%
performance left on the table with manual tuning
2-3 days
average setup time for optimization tools
The Solution
How TurboTune Solves This
Optimization as simple as adding a decorator
Add Decorator
Add one line to your training function
@turbotune.optimize(max_trials=100)Auto-Detection
We automatically detect hyperparameters and optimal ranges
learning_rate: 0.001 - 0.1
max_depth: 1 - 10
n_estimators: 50 - 300
Get Results
Watch your model improve in real-time
Best Score: 0.9847
Improvement: +23%
Key Benefits
- Zero configuration required
- Works with scikit-learn, XGBoost, LightGBM, Keras
- Intelligent Bayesian optimization
- Real-time progress tracking
- Pay only for what you use
TurboTune Dashboard
XGBoost Classification
RunningTrial 67/100Best Score: 0.9847
learning_rate: 0.042
max_depth: 7
n_estimators: 186
Comparison
Why Choose TurboTune?
See how we compare to existing solutions
| Feature | Manual Tuning | Optuna | Ray Tune | Weights&Biases | TurboTuneNEW |
|---|---|---|---|---|---|
| Setup Time | N/A | 2-3 days | 3-5 days | 1-2 days | 5 minutes |
| Code Changes Required | Many | Moderate | Many | Moderate | Minimal |
| Auto-Detection | Yes | ||||
| Cloud-Native | Partial | Yes | |||
| Pay-per-Use | Yes | ||||
| Bayesian Optimization | Advanced | ||||
| Multi-Objective | Limited | Yes | |||
| Real-time Dashboard | Basic | Basic | Advanced | ||
| Infrastructure Management | Manual | Required | Required | Partial | None |
Automatic Parameter Detection
Only platform that automatically detects hyperparameters and suggests optimal search ranges
Zero Infrastructure Setup
No infrastructure management required. Focus on your models, not DevOps
Pay-per-Optimization Pricing
First and only pay-per-use pricing model in the hyperparameter optimization space
Try TurboTune Free
Join early access and see the difference
Pricing
Simple, Transparent Pricing
Pay only for the compute you use. No hidden fees, no long-term contracts.
Free
Perfect for getting started
$0/month
- 1 concurrent optimization
- Up to 25 trials per job
- 60 compute minutes/month
- Community support
- Basic dashboard
- Priority support
- Advanced visualizations
Professional
For growing ML teams
$29/month
+ $0.02/compute minute
- 5 concurrent optimizations
- Unlimited trials per job
- $0.02/compute minute
- Priority support
- Advanced visualizations
- Performance analytics
- Export results
Enterprise
For large organizations
Custom
Contact for pricing
- Unlimited concurrent jobs
- Dedicated infrastructure
- Custom algorithms
- SLA guarantees
- Phone support
- On-premise deployment
- Custom integrations
Usage Calculator
Estimate your monthly cost with the Professional plan
Base plan:$29.00
Usage (300 minutes):$6.00
Estimated monthly cost:$35.00
TurboTune analyzes your model parameters and suggests optimal search spaces based on parameter types, your dataset characteristics, and learned patterns from similar models. Our ML system recognizes common hyperparameters like learning rates, regularization terms, and model architecture parameters, automatically setting appropriate bounds and distributions for each.
Currently TurboTune supports scikit-learn, XGBoost, LightGBM, CatBoost, and Keras/TensorFlow. We're continuously adding support for more frameworks. PyTorch support is coming in our next release. If you have a specific framework need, let us know during early access signup.
TurboTune requires no setup - just add a decorator and you're done. Unlike Optuna or Ray Tune, we handle automatic parameter detection, cloud infrastructure, and provide a pay-per-use pricing model. You don't need to write objective functions, manage trials, or configure search spaces manually. It's optimization made simple.
We're launching TurboTune in Q2 2025. Early access members get priority access to the platform, free optimization credits during beta, and a direct feedback line to our team. Join our early access program to be first in line when we launch.
Yes, absolutely. Your code runs in isolated containers, all data is encrypted in transit and at rest, and we never store your training data permanently. We follow enterprise-grade security practices with SOC2 compliance. Your intellectual property and sensitive data are completely protected.
Get Early Access to TurboTune
Join ML teams already using TurboTune to optimize their models
First access to TurboTune platform
Free optimization credits during beta
Direct feedback line to our team
Priority feature requests
Join 500+ ML engineers already optimizing models with TurboTune