Deploy your model
in minutes.
Train in Studio or Colab, import your model, and get a live API. Query with code, chat, or a spreadsheet.
import requests
response = requests.post(
"https://api.mldash.com/v1/predict/churn",
headers={"X-API-Key": "mldash_your_api_key"},
json={
"tenure_months": 24,
"monthly_charges": 79.99,
"support_tickets": 3
}
)
result = response.json()
print(result)Waiting for response...1.2M+
predictions served
48ms
avg response
5
ways to consume
Everything you need
From training to sharing, one platform handles it all.
Studio
Write Python in the browser, train any model, and export to ML-Dash.
SDK Export
Bring your own model from Colab or Jupyter. Export with mldash.export().
Instant API
Every model gets a REST endpoint automatically. Copy your key and go.
Chat Interface
Non-technical users query models in plain English via chat.
Explore & Share
Publish models to the community. Anyone can discover, try, and bookmark them.
Multi-Feature
Combine text, numerical, and categorical features in a single model.
Three steps to production
Go from a trained model to a live API in under 5 minutes.
Create
Train in Studio (our in-browser notebook) or in your own environment — Colab, Jupyter, VS Code.
Deploy
Import your model or export from Studio. Your REST endpoint is ready instantly with API key auth.
Share
Python, cURL, JavaScript, Dash AI chat, or the Explore marketplace. Your model, accessible to everyone.
Instant API endpoints
for every model
Every model you train gets a REST API endpoint automatically. No infrastructure to manage, no setup scripts to write.
import requests
response = requests.post(
"https://api.mldash.app/api/v1/predict/your-model-slug/",
headers={"X-API-Key": "mldash_your_api_key"},
json={
"text": "This product is amazing, highly recommend!",
"features": {
"category": "electronics",
"price": 29.99
}
}
)
result = response.json()
print(f"Prediction: {result['prediction']}")
print(f"Model: {result['model_name']}")48ms avg latency
Optimized inference pipeline for instant results
API key auth
Secure access with mldash_ prefixed keys and rate limiting
3 integrations
Python, JavaScript, and cURL examples included
Will this customer churn? They have been with us for 8 months, pay $89.99/month, and have opened 5 support tickets.
Based on the Customer Churn Predictor model, here is the prediction:
High Churn Risk
Confidence: 87%
Here are the key factors driving this prediction:
Talk to your models,
no code needed
Skip the API calls. Describe your data, get a prediction, and ask follow-up questions about what drove the result.
Describe your data conversationally, get structured predictions
Ask why a prediction was made and see feature importance scores
Test different inputs to see how predictions change
From notebook to API
Export from Studio or import from your notebook. Your endpoint is ready.
# In ML-Dash Studio (or Colab)
import mldash
# Export your trained model
mldash.export(
model=model,
name="my-model",
target={"label": "categorical"},
)
# Your API endpoint is ready at:
# /api/v1/predict/my-model/Works everywhere
you do
Every trained model gets a REST endpoint. Call it from Python, JavaScript, or cURL.
import requests
response = requests.post(
"https://api.mldash.app/api/v1/predict/your-model-slug/",
headers={"X-API-Key": "mldash_your_api_key"},
json={
"text": "This product is amazing, highly recommend!",
"features": {
"category": "electronics",
"price": 29.99
}
}
)
result = response.json()
print(f"Prediction: {result['prediction']}")
print(f"Model: {result['model_name']}")Explore community models
Browse models built by the community. Try before you integrate.
Customer Churn Predictor
ML-Dash Team
Predicts customer churn based on usage patterns, support tickets, and billing history.
Sentiment Analyzer
ML-Dash Team
Classifies text as positive, negative, or neutral with confidence scores.
House Price Predictor
Sarah Chen
Estimates residential property prices using location, size, and market conditions.
Email Spam Classifier
Alex Kim
Identifies spam emails using NLP and behavioral features with high recall.
Credit Risk Scorer
David Lee
Assesses credit risk for loan applications based on financial history.
Customer Segmenter
James Wilson
Segments customers into behavioral clusters for targeted marketing.
Frequently asked questions
Everything you need to know about ML-Dash.
Ready to share
your model?
Upload a trained model, get an API endpoint, and start making predictions. That simple.