Evaluating ML Models for Bias - Build an Explainable model using financial dataset
The workshop will give a quick introduction to ML as an optimization problem and the ML pipeline flow. The importance of having an explainable model. The implementation steps of building ML and enabling the fairness of the Model. Participants will get the hands on experience in developing the ML model using a financial dataset, how to persist the Model and how to enable the model for Fairness. After attending the workshop participants will learn about the explainability and fairness of AI models. They should be able to build a trusted AI post this workshop independently.
Speaker/Instructor:
Rajesh Jeyapaul, Client Technical Architect, IBM
Date: 17th October 2019
Time: 11:15 AM to 01:30 PM
Venue: Workshop Room 1, NIMHANS Convention Centre, Bangalore.
Exclusively for Platinum Pass holders. Please select this workshop while registering.
Objective of the workshop
The objective of this workshop is as below:
- Create and validate MachineLearning Model
- Persist the model
- Bind and score the Model for Fairness evaluation
Who can attend this workshop?
- ML Developers who have the knowledge of building a basic ML
- Anyone with basic Knowledge in Python
What all will be covered in the workshop
- A quick introduction to ML as an optimization problem and the ML pipeline flow. The importance of having an explainable model. The implementation steps of building ML and enabling the fairness of the Model.
Participants will get the hands on experience in developing the ML model using a financial dataset , how to persist the Model and how to enable the model for Fairness.
Benefits/Takeaways of this workshop for the attendees
The participants will learn about the explainability and fairness of AI models. They should be able to build a trusted AI post this workshop independently.
Pre-requisites to attend the workshop
- Access to IBM cloud. Cloud Registration link provided below
https://ibm.biz/Bdzub4 - Able to build and deploy a basic ML model using python notebook using IBM cloud. The sample baseline program approach is defined in the link below:
https://developer.ibm.com/tutorials/watson-studio-auto- ai/
Note: Participants are expected to complete the above 2 steps before the workshop. Attend the doubt clearing webinar on Oct 15th
About Speakers
Rajesh K Jeyapaul
Sr. AI Architect @ IBM Digital Labs, India. He leads the Artificial Intelligence (AI) solution enablement for clients who enter the Digital Journey. In his 25 years of technology journey, he has led multiple transformational projects on various technology stacks. He has co-authored 8 books. He also has 6 patents to his credit. He started his career with Indian Defense Research organization as Research Fellow.
Prateek Goyal
I did my M.Tech from IIIT Bangalore and joined IBM India Software Labs where I’ve worked on products like Watson Data Platform, Watson OpenScale in the last 2 years. In OpenScale my main focus is on developing capabilities for helping customers develop trust in AI models by detecting and removing the bias from their models. Also, working on a new offering in OpenScale which allows customers to have model risk management and have better control over the models they are deploying to production.
Rajesh Jeyapaul
Client Technical Architect, IBM
Client Technical Architect, IBM
Prateek Goyal
Software Engineer, IBM
Software Engineer, IBM