Difficulty: Beginner
Estimated Time: 10 minutes


In this tutorial, you will navigate the User Interface of the Skymind Intelligence Layer (SKIL). SKIL does model training and inference, as well as ETL jobs and other ML-related tasks. It connects to many data sources and streaming engines, and its predictions can be fed into a variety of applications downstream.

SKIL Conceptual Diagram

A Lightning Introduction to the SKIL UI

Step 1 of 2

Step 1

Deploying a model with SKIL

Before we dive into the SKIL UI, we'll run a little Python script to train a Keras model, save it to file and deploy it to SKIL:

python train_and_deploy.py

This scenario is about SKIL's UI, but if you're interested in what exactly the script does, click on it in the editor to the upper right. Here's how SKIL works on a high level:

SKIL conceptual model

Each SKIL task starts out by defining a workspace, where you and your colleagues do all your work. Think of a workspace as a project.

Within a workspace you define one or several experiments in which you run machine learning tasks. In an experiment you can define several models and once you're happy with the performance of a model, you can create a deployment. You then deploy your model to obtain a service. The train_and_deploy.py script does all of that for you, here is the key part:

skil_server = Skil()
work_space = WorkSpace(skil_server, name="My workspace")
experiment = Experiment(work_space, name="Keras experiment")
model = Model('model.h5', model_id="keras", experiment=experiment)
deployment = Deployment(skil_server, name="My deployment")
service = model.deploy(deployment)

You now have a deployed SKIL service at your disposal, let's see what that looks like in the SKIL UI!