During the pre-work section of this workshop, you create a project based on an existing project file. If, for some reason, you are not using the project zip file to create your project then you will not have all the assets (Jupyter Notebooks, CSV files, etc) necessary for the labs.

The Jupyter Notebook is connected to a PostgreSQL database, which is used to store Watson OpenScale data. The notebook is connected to Watson Machine Learning and a model is trained and deployed. Watson OpenScale is used by the notebook to log payload and monitor performance, quality, and fairness. Watch the Video. Prerequisites. An IBM Cloud Account.

Click on the Service Credentials tab on the left and then click New credential + to create the service credentials. 2019-04-26 · Drive fairer outcomes: Watson OpenScale detects and helps mitigate model biases to highlight fairness issues. The platform provides plain text explanation of the data ranges that have been impacted by bias in the model and visualizations that help data scientists and business users understand the impact on business outcomes. Watson OpenScale is used by the notebook to log payload and monitor performance, quality, and fairness.

Sample Output. Go to the instance of Watson OpenScale that you created and click Manage on the menu and then Launch Application. Choose the Insights tab to get an overview of your monitored deployments, Accuracy alerts, and Fairness alerts. Drop and re-create the IBM Watson OpenScale datamart instance and datamart database schema. Optionally, deploy a sample machine learning model to the WML instance. Configure the sample model instance to OpenScale, including payload logging, fairness checking, feedback, quality checking, drift checking, and explainability.

Thus IBM Watson OpenScale not only helps customers identify Fairness issues in the model at runtime, it also helps to automatically de-bias the models. In this post, we explain the details of how

You can generate these metrics on demand by clicking the Check fairness now button or by using the Python client. In IBM® Watson OpenScale, the fairness monitor scans your deployment for biases, to ensure fair outcomes across different populations. Requirements Throughout this process, IBM® Watson OpenScale analyzes your model and makes recommendations based on the most logical outcome.

24 Oct 2019 Manage fairness and bias in your AI models. Lindholmen High Visibility Fairness Examples AI Fairness 360 vs Watson OpenScale.

Connect and share knowledge within a single location that is structured and easy to search. Learn more Seats left: 13. AI Fairness and Explainability with Watson OpenScale on CloudPak for Data. This remote webinar with demo and hands-on labs will give the participant an understanding and practical experience of the AIs fairness, explainability, bias detection and mitigation provided by Watson OpenScale and Watson Machine Learning. During the pre-work section of this workshop, you create a project based on an existing project file. If, for some reason, you are not using the project zip file to create your project then you will not have all the assets (Jupyter Notebooks, CSV files, etc) necessary for the labs. You will learn how Watson OpenScale lets business analysts, data scientists, and developers build monitors for artificial intelligence (AI) models to manage risks.

IBM Watson® OpenScale™ tracks and measures outcomes from AI throughout it's lifecycle, and adapts and governs AI in changing business situations From the 'Model Monitors' tab, in the subscription tile you have created, click on one of the N/A values (i.e the N/A under the 'Fairness' heading). You will see some Analytics data, with the Date Range set to Today. We've just configured OpenScale to monitor our deployment, and sent a scoring request with 8 records, so there is not much here yet. Fairness metrics overview.
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Hence in a nutshell, IBM Watson OpenScale does not arbitrarily Model monitors allow Watson OpenScale to capture information about the deployed model, evaluate transaction information and calculate metrics.

2021-02-28 · OpenScale is configured so that it can monitor how your models are performing over time.
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Custom monitors consolidate a set of custom metrics that enable you to track, in a quantitative way, any aspect of your model deployment and business application. You can define custom metrics, and use them alongside the standard metrics, such as model quality, performance, or fairness metrics that are monitored in IBM Watson OpenScale.

You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. You will learn how Watson OpenScale lets business analysts, data scientists, and developers build monitors for artificial intelligence (AI) models to manage risks. You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. You will understand how to use Watson OpenScale to build monitors for quality, fairness, and drift, and how monitors impact business KPIs. You will also learn how monitoring for unwanted biases and viewing explanations of predictions helps provide business stakeholders confidence in the AI being launched into production. Craft fairs are a fun way to meet new people and potential clients. Whether you're a lover of local crafts or you wish to venture into selling your own products at craft fairs, use this handy guide to find upcoming craft fairs near you.