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The BoF will address four questions: Q1: What are novel modes of computing implied by AI? Which will have the greatest impact on CI for gateways and national compute resources? Facilitated by Joe Stubbs Q2: What are or should be the goals of broadening access to compute resources for AI purposes? Who can be brought into the community that previously was not? Facilitated by Sandra Gesing Q3: What are the interesting aspects related to data sharing? How do the various compute loads and modes impact how data are shared, moved, and accessed? Facilitated by Rob Quick Q4: How can AI be used in a science gateway to make it more effective, efficient, secure, or otherwise better? Facilitated by Claire Stirm 

The Tapis Streams API is a production grade quality service that provides REST APIs for storing, processing and analyzing real-time streaming data. This paper focuses on improvements made to Tapis 1.0 Streams API for making it up-to-date and easily accessible. The newer version, Tapis 1.2 Streams API adopts the latest version of InfluxDB, InfluxDB 2.X, which has built-in security features and supports next generation data analytics and processing with a data processing language Flux. This paper also discusses the measures implemented in the Tapis 1.2 Streams API to mitigate potential security risks involved in unauthorized data stream access by users who do not own it. Additionally, new data Channel Actions supporting 3rd Party notification and web-hooks has been released. Lastly a tool, Tapis UI, which is a self contained server less application to access Tapis Services via rest calls is discussed in the paper. Tapis UI is a lightweight browser only client application which allows interactive access to Streams resources and real-time streaming data. 

The goal of a robust cyberinfrastructure (CI) ecosystem is to catalyse discovery and innovation. Tapis does this through offering a sustainable production-quality set of API services to support modern science and engineering research, which increasingly span geographically distributed data centers, instruments, experimental facilities, and a network of national and regional CI. Leveraging frameworks, such as Tapis, enables researchers to accomplish computational and data-intensive research in a secure, scalable, and reproducible way and allows them to focus on their research instead of the technology needed to accomplish it. This project aims to enable the integration of the Google Cloud Platform (GCP) and CloudyCluster resources into Tapis- supported science gateways to provide on-demand scaling needed by computational workflows. The new functionality uses Tapis event-driven Abaco Actors and CloudyCluster to create an elastic distributed cloud computing system on demand. This integration allows researchers and science gateways to augment cloud resources on top of existing local and national computing resources.