we could manage it, For high reliability, we’d need a sacalable cloud infrastructure to handle increased traffic during live coding sessions. AWS or DigitalOcean could be used for this.
costs will increase based on usage. More users or live events = higher costs for compute power, storage, and bandwidth. Estimation depends on traffic, might see costs start at $100–$500/month for small-scale usage and increase to $1000+ for higher traffic or events.
for ~50 concurrent users we'd still need servers with enough CPU and RAM to handle that specially if we’re running a web-based IDE it would be around $100–$300/month on AWS
I was just after punch cards - but we still had to submit jobs through the control. The jobs would be run at some point later and we could pick up the results the next day.
That is why we use AWS EC2 Instance to deploy client's IoT dashboards to handle higher traffic generated by iot devices (to save server from crash). They are very scalable. I'm not suggesting the use of EC2 for this case but just saying that the similarity between device-to-server and client-to-server communication!
Hi tinyML would be a good place to start . Also, if you are looking at speech to text or something related to audio in AI, you can try Picovoice. For the two options, they have specific devices supported. I did a project using Portenta H7 on controlling things via voice commands.