Biases are imbalances in the accuracy of predictions across different groups, such as age or income bracket. Biases can result from the data or algorithm used to train your model. The field of machine learning provides an opportunity to address biases by detecting them in your data and model.

PurpleCube provides data to improve model quality through bias detection during data preparation and after training. PurpleCube also provides model explainability reports so stakeholders can see how and why models make predictions.

PurpleCube offers all the tools you need to create high quality training data. You can easily access data from us as well as third party data sources, label your data, automatically cleanse and transform data, and visualize data in order to engineer model features.

With PurpleCube’s data selection tool, you can quickly select data from multiple data sources. You can write queries for data sources and import data directly into PurpleCube from various file formats, and use the visualization templates and built-in data transforms to ensure data prepared will result in accurate ML models.

After data is prepared, PurpleCube provides all the tools you need to iteratively try different modelling techniques in order to evaluate their performance. You can pick different algorithms, including over 15 that are built in and optimized for PurpleCube, and over 150 pre-built models from popular model zoos available with just a few clicks. Inside PurpleCube Data Science, you can run the models on a small scale to see results and view reports on their performance so you can come up with high quality working prototypes.

PurpleCube also offers over 15 built in algorithms available in pre-built container images that can be used to quickly train and run inference.

PurpleCube supports reinforcement learning in addition to traditional supervised and unsupervised learning. PurpleCube has built-in, fully-managed reinforcement learning algorithms, including some of the newest and best performing in the academic literature.

PurpleCube provides everything you need to train and tune models. You can easily manage different training runs to isolate and measure the impact of changing data sets, algorithm version. PurpleCube automatically captures training input parameters, configurations, and results, and stores them as ‘experiments’. You can browse active experiments, search for previous experiments by their characteristics, review previous experiments with their results, and compare experiment results visually, model parameters or take advantage of automatic model tuning.

PurpleCube captures metrics in real-time so you can correct performance problems quickly before the model is deployed to production.

PurpleCube can automatically tune your model by adjusting thousands of different combinations of algorithm parameters to arrive at the most accurate predictions the model is capable of producing saving weeks of effort. Automatic model tuning uses machine learning to quickly tune your model to be as accurate as possible.

PurpleCube makes it easy to generate predictions by providing everything you need to deploy machine learning models in production and monitor model quality over time.

Many machine learning applications require humans to review low confidence predictions to ensure the results are correct.

PurpleCube eliminates the need to resize large datasets for batch processing jobs. Batch Transform allows you to run predictions on large or small batch datasets using a simple API.

PurpleCube provides a scalable and cost effective way to deploy large numbers of custom machine learning models. PurpleCube’s Multi-Model endpoints enable you to deploy multiple models with a single click on a single endpoint and serve them using a single serving container.