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We live in a connected world, and our digital experiences are formed by thousands of services acting as building blocks. As the world becomes more decentralized and services take over, cloud connectivity becomes the key differentiating factor of digital experiences for any organization.

Digital transformation is top of mind for many organizations. This concept can mean many things, but what does it mean for your organization? In this session, we'll walk you through some common patterns of successful transformations and highlight customer success stories for each pattern. We'll also show how inherent security risks in digital transformations are mitigated in these projects. The patterns we'll cover can be an inspiration on how to pursue your own digital transformation in practical terms.

It wasn't that long ago that simple allow or disallow rules on a network firewall was enough to satisfy security requirements. As application architectures have become more distributed - and "IP's" have given way to "Services", the way we control access between those resources is a critical issue to address. Policy becomes a first class citizen for controlling interactions in these environments.

We'll show how integrating the gateway into the Kuma and Kong Mesh installation allows users to access services, and how features like Layer 7 traffic routing and rate limiting allow users to stitch services together safely and securely. This webinar will show all this functionality live and hands-on so you can experience first hand how we build application connectivity in a cloud connected world. Join Cody De Arkland with Kong's Office of the CTO as we explore the new release of Kuma and Kong Service mesh.

The age of the load balancer has come to an end. The existing load balancer architecture pattern is a remnant of monolithic software deployments, and has no place in the decentralized world. In Kuma and Kong Service Mesh, we enable users to break free of the load balancers and build a connected fabric that allows users to span workloads across data centers and clouds.

Here at Kong, we talk a lot about microservices and "decentralized applications", but what does "decentralized" actually mean? Can we really drive modern application architectures with our services parked behind traditional load balancers, or is it time for us to rethink the model?

Data is the most precious resource of deep learning research. As such, it should be handled carefully, from data gathering, data annotation, data QA and data versioning. However, even if you managed to perform all the above tasks in the best possible way, data holds challenges that can dramatically affect your performance.

Among the many AI-driven corporate innovators in the world today, Siemens is one of the leading market players in the locomotives and mobility industries. Its platform, Railigent, serves as a solution for operators to use rail data intelligently, optimize their maintenance and operations, and guarantee one hundred percent availability.

Two main challenges are hindering the adoption of AI for enterprises and government agencies. The first is an increase in the need for hybrid solutions to manage data and data science applications, to address data locality in accordance with a rise in regulation and data privacy considerations. The second is an increase in first-hand experiences with the challenges and complexities involved in operationalizing machine learning, especially when considering hybrid deployment options, and when scaling data science across the organization. But good solutions exist to overcome these challenges - simple ways to work across hybrid cloud and edge environments without compromising on speed or performance.