Difficulty: Beginner
Estimated Time: 5 minutes


In this tutorial, you will learn how to train, deploy and query a TensorFlow model using the Skymind Intelligence Layer (SKIL). SKIL helps with model training, inference, ETL jobs and other data science tasks. SKIL connects to many data sources and streaming engines, and its predictions can be easily fed into downstream applications.

SKIL Conceptual Diagram

You did it -- great job!

You just ran and deployed TensorFlow model in five minutes. All you needed to do was:

  • Bring your own TensorFlow model
  • Define a SKIL workspace and experiment
  • Register your model in SKIL
  • Deploy the model as service and get predictions from it

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Deploy a TF Model with SKIL

Step 1 of 4

Step 1

Training and saving a TensorFlow 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 TensorFlow
  • Trains this model on MNIST data and stores the resulting model to model.pb

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.pb appear in the file tree of the editor window as the script runs.

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