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.

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

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

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

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

April 10 - Nerav Doshi  https://www.linkedin.com/in/nerav-doshi/

May 1 - Eric Martens https://www.linkedin.com/in/eric-martens-33835a7/

May 15 - Jacques Roy https://www.linkedin.com/in/jacques-roy/

Upkar Lidder, IBM Data Science and AI Developer Advocate, https://www.linkedin.com/in/lidderupk/

Watson AI technology 3-part series: Build, Run and Manage your AI Models