How to Deploy Vision AI Models in the Cloud | Serverless, Dedicated, Batch Processing
So you have a computer vision model... Now let's deploy it!
In this video, we briefly explore the hidden complexities of configuring your own cloud inference environments -- from provisioning GPUs to managing software dependancies. From there, we show how Roboflow's managed cloud handles this complicated "ballet" of infrastructure for you, allowing you to focus on building the best computer vision application for your use case.
We dive into the three primary options for deploying your trained models in Roboflow:
⚡️ Serverless API: Ideal for quick integration, automatic scaling, and getting started fast by simply plugging in an API key. You pay for the compute you use, and it scales up and down as needed.
🔋 Dedicated Deployment: Best for more predictable, consistent workloads where you need lower latency. This option provisions a persistent cloud server (CPU or GPU) specifically for you, keeping your models loaded in memory and ready to serve requests.
🌄 Batch Processing: The most cost-efficient option for when you can wait for results. This is suitable for asynchronous processing of large amounts of data, such as analyzing drone footage for inventory or asset inspection, where images/videos are processed in batches.
= What you'll see in this video =
00:00 Intro - You have a vision model. Now where to deploy it?
00:40 Why Roboflow Cloud? Get started quickly and reduce management overhead
03:23 What is the Serverless API?
04:22 How to use Serverless API with a Workflow
07:45 What is a Dedicated Deployment?
09:02 How to spin up a Dedicated Deployment
12:24 What is Batch Processing?
14:37 How to initiate a Batch Processing job
17:53 Summary and ending notes
= Additional Resources =
💡 Try Roboflow today
https://roboflow.com/
💡 Roboflow Managed Deployments Overview
https://docs.roboflow.com/deploy/roboflow-managed-deployments-overview
💡 Serverless API
https://docs.roboflow.com/deploy/serverless-hosted-api-v2
💡 Dedicated Deployments
https://docs.roboflo
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Chapters (9)
Intro - You have a vision model. Now where to deploy it?
0:40
Why Roboflow Cloud? Get started quickly and reduce management overhead
3:23
What is the Serverless API?
4:22
How to use Serverless API with a Workflow
7:45
What is a Dedicated Deployment?
9:02
How to spin up a Dedicated Deployment
12:24
What is Batch Processing?
14:37
How to initiate a Batch Processing job
17:53
Summary and ending notes
🎓
Tutor Explanation
DeepCamp AI