How can I scale machine learning inference effectively?

How can I scale machine learning inference effectively?

Scaling ML inference involves optimizing your model deployment to handle increased loads efficiently. What specific challenges are you facing?

I'm dealing with high latency during peak usage times.

You might consider using techniques like model quantization, batching requests, or deploying on a more powerful infrastructure. Have you tried any of these?

I've tried batching, but it didn't help much.

In that case, you could explore using a load balancer to distribute requests across multiple instances of your model. Would you like more details on that?

Yes, that sounds interesting! How do I set up a load balancer?

Setting up a load balancer typically involves configuring it to route traffic to your model instances. You can use services like AWS Elastic Load Balancing or NGINX. Would you like a step-by-step guide?