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Watson AI technology series: Build, Run and Manage your AI Models – Part I

April 10, 2020 8:30 am MST

Building and managing AI models is now becoming the key competency for application developers and software engineers.  Collaborating with data scientists and ML engineers, the application and DevOps teams are gaining skills and becoming active participants for the AI lifecycle management.  Powered by AutoAI from Watson AI technology, you can build models with Watson Studio, run models with Watson Machine Learning and measure models with Watson OpenScale.


What you will learn

In this 3-part Watson AI technology series, you will learn:
AI app use case patterns using prediction and optimization
Basics of machine learning and decision optimization
Monitoring AI models for fairness, accuracy, and drift 
Automating AI lifecycle management
April 10 – Using prediction and optimization to build apps with Nerav Doshi
Primer on AI and machine learning use cases
Create schedules and plans using the prediction and optimization 
Build and deploy app using Python Client  API 

May 1 –  Manage, measure and explain AI models with Eric Martens
Considerations in deploying models.
Using a graphical user interface or code, monitor those models for fairness, accuracy, and drift 
Make model explanations available to applications so your users can benefit from enhanced prediction 

May 15 –  AutoAI deep dive with Jacques Roy
AutoAI process – automated data processing, model selection, feature engineering and hyper parameter optimization
Visualize and understand models generated by AutoAI
Extract and further manipulate code from AutoAI to deploy with your app


Who should attend

Developers, data scientists and architects. Anyone interested in building and deploying AI models.

Prerequisite (free)


April 10 – Nerav Doshi
May 1 – Eric Martens
May 15 – Jacques Roy


Upkar Lidder, IBM Data Science and AI Developer Advocate,