If you have a trained machine or deep learning model and want to deploy it on our platform, check out the Model Deployment Guides where you'll see tutorials for popular frameworks such as Scikit-learn (including a hands-on video), PyTorch, and Tensorflow.
Learn how to create and publish your algorithmsGet Started Now
If you have a trained machine or deep learning model and want to deploy it on our platform, check out the Deploy Model Guides where you'll see tutorials for popular frameworks such as Scikit-learn, PyTorch, and Tensorflow.
Best Practices and Deep Dives for Algorithm Development and Model Deployment
Follow these tips to create the best algorithm profile possible.
How to handle errors between algorithms.
Create, Update, Publish, and Delete Algorithms via the Algorithmia API
Using CI/CD to continuously deploy your ML models via Jenkins, GitHub Actions, or GitLab CI/CD
Reloading your ML model file when it changes
Controlling where the source code for your algorithm resides
Matrix of supported algorithm runtime environments
Getting your algorithm on Algorithmia is easy. Find out how with this tutorial.
Markdown syntax for Algorithm descriptions.