Fueling innovative software
July 15-18, 2019
Portland, OR

Building a secure and transparent ML pipeline using open source technologies

1:30pm5:00pm Tuesday, July 16, 2019
Secondary topics:  AI Enhanced
Average rating: *....
(1.80, 5 ratings)

Who is this presentation for?

  • Data scientists and researchers, ML engineers, and DevOps practitioners

Level

Intermediate

Description

The application of AI algorithms in domains such as criminal justice, credit scoring, and hiring holds unlimited promise. At the same time, it raises legitimate concerns about algorithmic fairness. There’s a growing demand for fairness, accountability, and transparency from ML systems. And we need to remember that training data isn’t the only source of possible bias and adversarial contamination. It can also be introduced through inappropriate data handling, inappropriate model selection, or incorrect algorithm design. We need a pipeline that’s open, transparent, secure, and fair, and that fully integrates into the AI lifecycle. Such a pipeline requires a robust set of bias and adversarial checkers, debiasing and defense algorithms, and explanations.

Animesh Singh, Svetlana Levitan, and Tommy Li demonstrate how to build an ML pipeline that’s open, secure, and fair and that fully integrates into the AI lifecycle, using open source tools like AIF360, ART, Fabric for Deep Learning (FfDL), Model Asset Exchange (MAX), and Seldon Core.

Prerequisite knowledge

  • A basic understanding of Python
  • General knowledge of ML (useful but not required)

What you'll learn

  • Learn how to use several open source projects to help build an open, transparent, secure, and fair machine learning pipeline
Photo of Animesh Singh

Animesh Singh

IBM

Animesh Singh is a senior technical staff member (STSM) and program director for IBM Watson and Cloud Platform, where he leads machine learning and deep learning initiatives on IBM Cloud and works with communities and customers to design and implement deep learning, machine learning, and cloud computing frameworks. He has a proven track record of driving design and implementation of private and public cloud solutions from concept to production. Animesh has worked on cutting-edge projects for IBM enterprise customers in the telco, banking, and healthcare industries, particularly focusing on cloud and virtualization technologies, and led the design and development first IBM public cloud offering.

Photo of Svetlana  Levitan

Svetlana Levitan

IBM

Svetlana Levitan is a developer advocate with the IBM Center for Open Source Data and Artificial Technologies (CODAIT). Previously, she was a software engineer implementing SPSS statistical and machine learning algorithms. She earned her PhD in applied math and MS in CS from University of Maryland. She loves learning more on AI and sharing her knowledge.

Photo of Tommy Li

Tommy Li

IBM

Tommy Li is a software developer at IBM focusing on cloud, container, and infrastructure technology. He’s worked on various developer journeys that provide use cases on cloud-computing solutions, such as Kubernetes, microservices, and hybrid cloud deployments. He’s passionate about machine learning and big data.