- Abstract: We will show the advantages of using a fabric of open source AI services and libraries, which have been launched by the AI labs in IBM Research, to train, harden and de-bias deep learning models. The motivation is that model building should not be monolithic. Algorithms, operations and pipelines to build and refine models should be modularized and reused as needed. The componentry presented meets these requirements and shares a philosophy of being framework- and vendor- agnostic, as well as modular and extensible. We focus on multiple aspects of machine learning that we describe in the following. To train models in the cloud in a distributed, framework-agnostic way, we use the Fabric for Deep Learning (FfDL). Adversarial attacks against models are mitigated using the Adversarial Robustness Toolbox (ART). We detect and remove bias using AI Fairness 360 (AIF360). Additionally, we publish to the open source developer community using the Model Asset Exchange (MAX). Overall, we demonstrate operations on deep learning models, and a set of developer APIs, that will help open source developers create robust and fair models for their applications, and for open source sharing. We will also call for community collaboration on these projects of open services and libraries, to democratize the open AI ecosystem.
- TL;DR: Introduction to IBM's Fabric for Deep Learning, an open source platform based on Kubernetes to train and postprocess deep learning models.
- Keywords: deep learning, fabric, FfDL, MLaaS, cloud, Watson, IBM, kubernetes