Welcome to the "AI Trust, Bias and Explainability" learning series, by IBM AI. In collaboration with IBM team, we host a series of practical introductory sessions to AI trust, bias and explainability.
This is the 8th session:
An emerging trend in AI is the availability of automation technologies that train several models and select the one with best-fit. This automated AI process includes several variations of feature engineering and hyperparameter optimization that aim to improve the model.
In this lab, you will use the Watson Studio AutoAI tool to build a rapid prototype and generate a Python notebook for your prototype. You will then examine each of the steps in the Python notebook to see how the AutoAI performed.
All sessions of the series:
Jul 27th - AI Security Privacy-Preserving Machine Learning by IBM AI. Session 1
Aug 10th - Explainable AI Workflows using Python. Session 2
Aug 17th - Understanding and Removing Unfair Bias in ML. Session 3
Aug 24th - Adversarial Robustness 360 Toolbox For ML. Session 4
Aug 31st - Workshop: Explainable AI Workflows. Session 5
Sep 9th - Workshop: Explainable AI Workflows. Session 6
Sep 21st - Workshop: Explainable AI Workflows. Session 7
Dec 03, 10:00 AM PST 
