Thank you for starting your first machine learning project on the SubmitX Collaboration platform . The platform is hosted on public cloud and you have to login to the server through the SubmitX Visual Modeller client to run you machine learning jobs. If you have not downloaded the client yet in your desktop/laptop, you can download it from here and follow the installation and configuration instruction.

After successful login, the menu-bar of the remote client would look like as shown below.Verify that your registered first name and last name appears at the top.

Figure 1 : The remote client menu-bar

Upload raw file

Click on SandBox -> Upload Raw File in the menu-bar.

Choose a csv (or comma separated value) file and press Open.Once the raw file is successfully uploaded, it will be visible under “rawfiles” under the “Sandbox” tree in the left navigation panel.

Open an empty model

Click on New button in the menu-bar

An empty model would be created named “Untitled”. If you are not interested in creating new features from your raw data, press the Auto Create button in the menu-bar, otherwise for creating new features, go here.

Auto-Create model

Click on Auto Create button in the menu-bar

Select the same raw csv file (as uploaded to sandbox in previous step) from which the model has to be created. If the raw data is a time series data, choose time series in the Column Significance dialog and select the predictor, index and time column(s). For cross-section data choose the predictor column only. The IDE guesses the column type as either Categorical or Continuous . To change it, double click on the node and select the appropriate NEURONSTATETYPE.

A raw data is represented as sensory neuron and a predictor is represented as action neuron . A raw data must be converted to a feature or interneuron before it can be connected to an action neuron. It can be transformed to a feature using the feature engineering nodes.

Save the model

Click on File -> Save in the menu-bar

Before saving the model, update the RAWFILE field of the action neuron with the name of the raw csv file.The model is saved in the sandbox with extension .brn and an unique identifier called BRNID is assigned to the model.The BRNID of the model is visible in the bottom panel of the left navigation bar.

Learn using Autopilot

Click on Tune button in the menu-bar

Select AUTOPILOT as the Supervised Learning Algorithm. Press Learn

Note : You can choose a different algorithm other than “Autopilot” from the drop-down menu. In that case, you have to manually enter the value of the hyper-parameters of the chosen algorithm in the respective algorithm tab.

There is no limit on the number of machine learning jobs that you can submit to the SubmitX collaboration platform.You can try out multiple combination of algorithms and their respective hyperparameters till you are satisfied with the model diagnostics. You have to refresh the Task-Viewer manually to fetch the latest status of the running jobs from the server.

Autopilot is not an algorithm by itself but it helps to choose the best algorithm. It also gives advice on the value of the hyperparameters of the chosen algorithm.

View Log files

Double-click on the log file in the left navigation bar.

The log file will be downloaded into the <INSTALL_DIR>/downloads directory.

The file naming convention is log_<BRNID> _<Task ID>_<YYYY>-<MM>-<DD>-<hh>-<mm>-<ss>.log

Deploy/Publish model

Click on Tune button in the menu-bar

Select AUTOPILOT as the Supervised Learning Algorithm. Press Deploy

Note : You can choose a different algorithm other than “Autopilot” from the drop-down menu. In that case, you have to manually enter the value of the hyper-parameters of the chosen algorithm in the respective algorithm tab.

The difference between Deploy and Learn is that Deploy updates the model memory with the learned parameters whereas Learn doesn’t.
After a model is deployed and relevant access permissions are granted, it can be used by any external system for prediction and forecast through RESTful APIs

Verify Predict (Postman)

API

celeriacmldevops3.ap-south-1.elasticbeanstalk.com/knowledge-hotline/knowledge/predictCategorical/{brnid}/{viewid}

brnid is the BRNID of the model and the viewid is the ID of the red node or the predictor node.Hover your mouse over the red node and you’ll find the ID in the tooltip.