Deploy an operational AI model

Classification problems have always been around. Humans have in all times tried to classify the world around them. We try to make sense of the world by putting labels on things, this is a "cat," this is a "dog," this is a "bird". But classification is hard for most real-world problems, it’s not always easy to draw a dividing line between one class or another. Thanks to AI this has now become much easier. With increased computing power, big data and deep learning models we can now solve hard classification much faster and more accurately.

The MNIST classification problem is the “Hello world” of deep learning. We’re going to take it one step further and show how you can make a real-world AI web service out of this little example dataset.

The original MNIST dataset consists of small, 28 x 28 pixel images of handwritten numbers that are annotated with a label indicating the correct number.

The MNIST dataset we’re using in this tutorial consists of 3-channel RGB pictures, because then you can use the deployed experiment with a phone.
Important note! For a model to be usable, the input data needs to be of the same type as the model was trained on. In this case, the picture that depicts a number needs to be in RGB (a standard digital format.) This is true for every AI-model – it can’t predict apples when it has been trained on oranges.

First, create a project and name it so you know what kind of project it is. Naming is important.

A project combines all of the steps in solving a problem, from the pre-processing of datasets to model building, evaluation and deployment. Using projects makes it easy to collaborate with others.

After creating the project, you will be taken to the Datasets view, where you can import data.

Click the Data library button and look for the MNIST - tutorial data dataset in the list. Click on it to get more information.

If you agree with the license, click Accept and import. This will import the dataset in your project, and you will be taken to the dataset’s details where you can edit features and subsets.

It is now time to create an experiment in the Modeling view. An experiment contains all the information needed to reproduce the experiment:

The result from this experiment is a trained AI model that can be evaluated and deployed.

The experiment is done and ready to be trained. All settings, e.g., batch size, learning rate, target feature, have been pre-populated by the platform. The next step is simply to click Run. So let’s do that!

Click Run in the top right corner to start the training.

Navigate to the Evaluation view and watch the model train. You can see how the loss gets lower epoch by epoch.

This experiment is great, but it’s of no use as long as it is locked up inside the Peltarion Platform. If you want people to use the trained experiment, you have to get it out in some usable form. This is where the Deployment view comes in. In this tutorial we will deploy the model as API.

Click the Test deployment button. The following page will show up:

In this tutorial you’ve created an AI experiment that you trained, evaluated, and deployed. You have used all the tools you need to go from data to production — easily and quickly.

You’ve used one labeled dataset and a CNN to predict numbers, but can you improve a result by using multiple sets of input data, both tabular data and images. Try this in the tutorial Predict California house prices. Predicting a house price from both tabular and image inputs is a unique problem and not something you can do with anything other than deep learning.