Artificial intelligenceData ScienceMachine Learning
by Alex Jones Published December 20, 2018
IBM Watson OpenScale is an open platform that enables organizations to automate and operate their AI across its full lifecycle. Watson OpenScale provides a powerful environment for managing AI and ML models on IBM Cloud, IBM Cloud Private, or other platforms. It offers the following benefits:
Open by design: Watson OpenScale provides insights into the health of ML and DL models – performance, as well as accuracy and fairness of outcomes – built using any frameworks or IDEs, and deployed on any model-hosting engine.
Fairer outcomes: Watson OpenScale detects and helps mitigate model biases to highlight possible fairness issues. The platform provides plain text explanations of the data ranges that have been impacted by bias in the model, and visualizations to help data scientists and business users understand the impact on business outcomes. As biases are detected, Watson OpenScale automatically creates a de-biased companion model that runs beside the deployed model, thereby previewing the expected fairer outcomes to users without replacing the original model.
Explanation of transactions: Watson OpenScale helps enterprises bring transparency and auditability to AI-infused applications by generating explanations for individual transactions, including the attributes that were used to make the prediction and weightage of each attribute.
Automated AI creation: Neural Network Synthesis (NeuNetS), available in this update as a beta, automatically synthesizes neural networks by fundamentally architecting a custom design for a given data set. In the beta, NeuNetS will support image and text classification models. NeuNetS reduces the time and lowers the skill barrier required to design and train custom neural networks. This makes those networks more accessible to non-technical subject matter experts and helps data scientists be more productive.
The Monitor WML models with Watson OpenScale pattern builds on the Prediction using Watson Machine Learning pattern. It uses the model created in that previous pattern as an example for management usingWatson OpenScale.
The Monitor custom machine learning engine with Watson OpenScale pattern describes a method to use Watson OpenScale and a custom machine learning model serving engine. It explains how to log a payload for a model deployed on a custom model serving engine using Watson OpenScale Python SDK, and uses Keras to build a deep learning REST API and monitor withWatson OpenScale.
We hope you’ll use these patterns to ramp up your AI and machine learning skills. Please try them out and let us know what you think.
For more information on Watson OpenScale, see the following pages:
Get the Code »
Artificial intelligenceJupyter notebook+
Back to top