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Amazon Creates New Tool To Engineer AI Models With Just A Few Lines Of Code




As efforts to make machine learning easier more accessible increase, different companies are creating tools to make the creation and optimization of deep learning models simpler. As VentureBeat reports, Amazon launched a new tool designed to help create and modify machine learning models in just a few lines of code.

Carrying out machine learning on a dataset is often a long, complex task. The data must be transformed and preprocessed, and then the proper model must be created and customized. Tweaking the hyperparameters of a model and then retraining can take a long time, and to help solve issues like this Amazon has launched AutoGluon. AutoGluon is an attempt to automate much of the overhead that typically comes with the creation of a machine learning system. For instance, not only do machine learning engineers have to decide on an appropriate architecture, they also need to experiment with the hyperparameters of the model. AutoGluon endeavors to make both the creation of the neural net architecture and the selection of appropriate hyperparameters easier.

AutoGluon is based on work initially begun by Microsoft and Amazon in 2017. The original Gluon was a machine learning interface designed to let developers mix and matched optimized components to create their own models, but AutoGluon just creates a model end-to-end, based on the desires of the user. AutoGluon is reportedly capable of producing a model and selecting the hyperparameters for the model, within a range of specified choices, with as few as three lines of code. The developer only has to provide a few arguments like their desired training completion time, and AutoGluon will calculate the best model that will complete within the specified runtime and given the available computation resources.

AutoGluon is currently capable of creating models for image classification, text classification, object detection, and tabular prediction. AutoGluon’s API is also intended to allow more experienced developers to be able to customize the auto-generated model and improve performance. At the moment, AutoGluon is only available for Linux and it requires Python 3.6 or 3.7.

Jonas Mueller, part of the AutoGluon development team, explained the reasoning behind the creation of AutoGluon:

“We developed AutoGluon to truly democratize machine learning, and make the power of deep learning available to all developers. AutoGluon solves this problem as all choices are automatically tuned within default ranges that are known to perform well for the particular task and model.”

AutoGluon is a new method within a long line of methods intended to reduce the expertise and time needed to train machine learning models. Software libraries like Theano automated the calculation of gradient vectors, while Keras let developers easily specify certain desired hyperparameters. Amazon believes that there is still more ground that can be covered when it comes to democratizing machine learning, like making data pre-processing and hyperparameter tuning simpler.

The creation of AutoGluon seems to be part of an effort by Amazon to make training and deploying machine learning systems easier and more accessible. Amazon has also made machine learning-centric changes to its AWS suite. For example, upgrades have been made to the AWS Sagemaker toolkit. The AWS SageMaker toolkit within the AWS suite lets developers train and deploy models to the cloud. SageMaker comes with a variety of tools that let developers automatically choose algorithms, train and validate models, and improve the accuracy of models.