We are excited to announce the addition of TensorFlow to the Loominus machine learning Provider Plugin architecture! Loominus has taken the complexity out of hand coding TensorFlow models and made it easier for anyone to experiment with neural networks on classification and regression problems. In this post we’ll discuss how to use TensorFlow for Classification and Regression models in Learner.

To build a TensorFlow model, simply create a Predictive Model project in Learner and then go through the guided steps to configure your project. Check out our eight minute getting started video.

At the Model Algorithm step, select the TensorFlow provider and choose the algorithm you want to use. Finally, click Train Model.

For classification problems, you can choose from the following TensorFlow algorithms:

For classification problems, you can choose from the following TensorFlow algorithms: DNNClassifier, DNNClassifierV2, and BoostedTreesClassifier

Their counterparts for regression problems are:

Their counterparts for regression problems are DNNRegressor, DNNRegressorV2 and BoostedTreesRegressor.

Once Learner finishes configuring your project with its first model, the project’s status will transition to the Ready state. You’ll then be able to view the model results and train additional models.

Here Learner is training two new TensorFlow models concurrently.

Here Learner is training two new TensorFlow models concurrently.

Happy modeling 🙂

About Loominus

Loominus is an end-to-end platform that helps teams ingest and stage data, build advanced machine learning models with no code and deploy them into production. Loominus makes it easy for individuals and teams without experience building machine learning pipelines to take advantage of machine learning faster. Loominus is equally great for experienced data scientists that need to focus on model selection and tuning.

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