Drascalita Haut: Today we're going to talk about functions, and in particular Functions as a Service. It applies it to AI in order to present a solution that seems to bring strategic advantages when deploying AI services at scale. During this session, it may feel like we're dancing a bit, moving through tools, new technologies, maybe you might even see some new steps like workflows or methods to work with AI. And for those of you that know salsa, you know that it starts with a step forward. So today, I'm going to start with some bold statements, but bear with us, I'm going to take a step back, and then me and AK are going to rehearse something through a live demo, which hopefully is going to go just fine, to illustrate what we're talking about. Let me start with a step forward, FaaS value prop. What does FaaS bring that more and more people are talking about? I came with three reasons. Number one is FaaSter to prototype, FaaSter to create services, because we work with code, with functions, just code, and we just push the code as it is. Second, never pay for idle. FaaS platforms have the capability to shut down the parts of the system that are not used, so we don't incur any cost. And the third one is a low maintenance overhead. That's because FaaS platforms usually take away the burden to create containers, keeping them up to date, apply security updates, auto-scaling the functions, deploy them in multiple regions. In other words, FaaS boldly claims that you will find it easier to build more services, and you're going to pay less. Now, this is a pretty bold statement, isn't it? So allow me to take a step back and look at how developers are producing microservices today. A few years ago, we realized that microservices are better than monoliths because in essence, they add flexibility, and they simplify the experience. At the same time, it's also less risky to independently update parts of the system. And I would assume that many of us know what microservices are. A very high-level microservice architecture is in this slide. So the final solution basically consists of isolated pieces, with its own independent deployment lifecycle. Now, microservices used to be deployed in their own VMs, and then containers came and it was such a revolution because we're able to correctly run multiple services in isolation in the same VM.