Difficulty: Beginner
Estimated Time: 5 minutes


In this tutorial, you'll learn how to train, deploy, and query a Keras model with the Skymind Intelligence Layer (SKIL). SKIL supports model training, inference, ETL jobs and other tasks. It connects to data sources and streaming engines, and its predictions can be fed to downstream applications.

SKIL Conceptual Diagram

You did it. Great Job!

You just trained and deployed a Keras model with SKIL.

Request a Demo

Wanna learn more about how SKIL can make your life easier? Contact us to request a demo!

Get Updates About Future Tutorials

Deploy a Keras Model with SKIL

Step 1 of 4

Step 1

Training and saving a Keras model

To deploy a deep learning model in production, you need to train it. The Python script train.py :

  • Loads the MNIST dataset containing handwritten digits and their labels
  • Defines a small Multilayer Perceptron in Keras
  • Trains this model on MNIST data and stores the resulting model to model.h5

You can click on train.py in the editor window for details. Or you can run this script by clicking here:

python train.py

You'll see model.h5 appear in the file tree of the editor window as the script runs.

With that, we can deploy the Keras model with the Skymind Intelligence Layer (SKIL).