Welcome to SlashML documentation

SlashML is a framework that provides the best performing ML models under one API. This enables the users to rapidly prototype their machine-learning solutions.

Quick Start

To quickly play around with the model we recommend using the Python SDK client. You can install the client using pip.

pip install slashml

Example - LinearRegression

from slashml import ModelDeployment
import time
from sklearn.linear_model import LinearRegression
my_model = LinearRegression()[[1], [2], [3]], [2, 4, 6])
# Replace `API_KEY` with your SlasML API token.
client = ModelDeployment(api_key=None)
# deploy model
response = client.deploy(model_name='linear_model', model=my_model)
# wait for it to be deployed
status = client.status(
# submit prediction
model_input = [[512]]
prediction = client.predict(, model_input=model_input)
print(prediction) # [1024.0]


You can find production ready examples here.

API Token

There is a daily limit (throttling) on the number of calls the user performs. The code can run without specifying the API key. The throttling kicks in and prevents new jobs after exceeding 10 calls per minute.

If the user intends on using the service more frequently, it is recommended to generate a token or API key from here. You can pass the API key when creating a model, if you don't the API will still work but you will be throttled.

Deploying the Examples

Deploy Demos: It takes around 2-5 minutes to deploy our demos.

Additional Resources

Additional resources we think may be useful as you develop your application!

  • Website: Visit our website to manage your slashml account.
  • Twitter: Follow us on twitter to receive the latest updates!


Select a feature below to learn more about it.


Docs for SlashML



Specific examples of how to deploy models