Let's deploy a custom AI model container as an autoscaling API on your private cloud in 10 minutes

william falcon · Intermediate ·📰 AI News & Updates ·1y ago

Skills: Model Deployment90%

I show how to deploy a container already built and published to any container registry as an autoscaling API for AI models. The server can scale to zero (serverless) when idle (ie: zero $) and scale up on demand as requests come in. 00:30 - Find the Deploy API button 00:43 - find your private or public container. 00:52 - authenticate the container 01:11 - configure autoscaling (serverless, scale to zero) 01:30 - configure autoscaling to handle high traffic 01:45 - advanced settings (for kubernetes experts) 01:57 - choose the cloud account 02:30 - add container commands 02:48 - add a health check (optional) 03:00 - configure autoscaling rules (optional) 03:20 - deploy the model 03:40 - how to share (and collaborate) 04:00 - how to monitor requests, uptime, latency 04:05 - analyze cold start 05:00 - view audit events 05:20 - release a new container version 05:50 - view replica telemetry 06:22 - build a request to test the server 07:20 - summary, get a free account with pay as you go

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Chapters (19)

0:30 Find the Deploy API button

0:43 find your private or public container.

0:52 authenticate the container

1:11 configure autoscaling (serverless, scale to zero)

1:30 configure autoscaling to handle high traffic

1:45 advanced settings (for kubernetes experts)

1:57 choose the cloud account

2:30 add container commands

2:48 add a health check (optional)

3:00 configure autoscaling rules (optional)

3:20 deploy the model

3:40 how to share (and collaborate)

4:00 how to monitor requests, uptime, latency

4:05 analyze cold start

5:00 view audit events

5:20 release a new container version

5:50 view replica telemetry

6:22 build a request to test the server

7:20 summary, get a free account with pay as you go

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