Past Webinars

Improve workflow and deliver value faster with Kanban

Traditional management techniques have emphasized Utilization as a key metric to measure team productivity. Agile and Lean management techniques, instead, focus on decreasing cycle time and increasing flow by reducing Work In Process (WIP) and building in Slack. In this presentation, we will take a look at the correlations between these seemingly unrelated data points and how teams can leverage it to deliver value faster. We show how Kanban is a very effective tool to visualize your workflow, control WIP and increase reduce bottlenecks in your system.

How to Create User Friendly Web Based Forms

In this highly interactive session you will gain an understanding of how to apply key principles of user interface design to produce easy-to-use web-based forms. We will cover techniques for creating “conversational” forms using simple language that align with how users read and process information online. You will learn how to organize the form fields to minimize user interaction so they follow a logical flow. We will also discuss how to present error and informational messages and the importance of being consistent while leveraging design patterns in your form design. This webinar is geared towards an audience who has a beginner or intermediate-level knowledge of usability and user experience design but would like to learn these skills to improve the quality of their work.

Real World Security Issues with Machine Learning

The first part of this presentation will examine the security vulnerabilities of machine learning systems from algorithm design to training methodologies to operational deployment. You will learn about the types of attacks that could be used to subvert, compromise or render machine learning systems inoperable. We will cover the risk implications, which are staggering, and look at how mission critical these systems can be without proper testing and security implementation. The second part of this presentation will describe how many of the security protocols, testing methods and best practices in other areas can be adapted to develop robust security models for machine learning systems.