Viridiana Lourdes explains how banks and financial enterprises can adopt and integrate actual risk models with existing systems to enhance the performance and operational efficiency of the financial crimes organization. Join in to learn how actual risk models can reduce segmentation noise, utilize unlabeled transactional data, and spot unusual behavior more effectively.

Design thinking is a methodology for creative problem-solving developed at the Stanford d.school. The methodology is used by world-class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung, and GE. Michael Radwin prepares a recipe for how to apply design thinking to the development of AI/ML products.

Purpose, a well-defined problem, and trust are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.

Domain insights are crucial for successful AI/ML initiatives. This talk discusses three areas of concerns: clarification of business context, awareness of nuances of data sources, and navigating organizational structure.

The Jupyter Notebook has become the de facto platform for data scientists and AI engineers to build interactive applications and develop AI/ML models. Luciano Resende details how to schedule related notebooks that correspond to different phases of the model lifecycle into notebook-based AI pipelines and walks you through scenarios that demonstrate how to reuse notebooks via parameterization.

Arm and Linaro launched the AI initiative one year ago to collaborate on an open source inference engine common to all Arm edge devices and support SoC specific NN acceleration via a plug-in back end …

Machine learning can be challenging to deploy and maintain. Any delays in moving models from research to production mean leaving your data scientists' best work on the table. Arif Wider and Emily Gorcenski explore continuous delivery (CD) for AI/ML along with case studies for applying CD principles to data science workflows.

The promise of SDN has always been applications that can communicate behaviors and needed resources with the SDN controller via a well defined API. While traditional methods to determine the needed be …

Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment.

Are machines ready to write, test, debug and patch enterprise software? What are the emerging capabilities? Is it a fantasy or fast happening already? How are enterprises strategizing and retooling?Gi …

AI/ML applications on Kubernetes can be optimized for performance at many levels.This presentation provides an overview of the optimizations such as:- Distributed training on multiple GPUs with optima …

In this session, we will discuss how Kubernetes driven AI/ML building blocks are making AI/ML simple, fast and efficient for data scientists, data engineers, devops engineers and everyday users. We wi …