Optimizing AI/ML Inference Workloads for Production: A Practical Guide | by Nicholas Thoni | Mar, 2025

In in the present day’s AI-driven world, deploying machine studying fashions to manufacturing presents a singular set of challenges. Engineering groups usually discover themselves caught between needing the sturdy orchestration capabilities of Kubernetes and fighting its operational complexity.

This text explores sensible methods for optimizing AI/ML inference workloads in manufacturing environments, specializing in how specialised infrastructure can dramatically enhance each efficiency and cost-efficiency.

ML deployments in manufacturing face a number of vital challenges:

• Useful resource-intensive computation necessities that differ considerably from conventional net purposes
• Unpredictable site visitors patterns requiring versatile scaling capabilities
• {Hardware} optimization wants that commonplace infrastructure setups don’t handle
• Useful resource competition points when ML workloads share infrastructure with different purposes

For a lot of groups, these challenges have meant both constructing intensive in-house DevOps experience or accepting important compromises in efficiency and price.

The important thing to optimizing ML inference deployments lies in workload placement — the flexibility to outline exactly the place and the way your ML providers run inside your infrastructure.

Efficient workload placement allows:

1. Useful resource optimization based mostly on the precise wants of ML workloads
2. Workload isolation to stop useful resource competition
3. Value effectivity by way of right-sized, purpose-built infrastructure
4. Efficiency enhancements by matching {hardware} to computational necessities

Let’s have a look at implement this in observe.

Step one is creating devoted node teams optimized for ML workloads. Right here’s what this sometimes entails:

[
{
"type": "g4dn.xlarge",  // ML-optimized instance type
"disk": 100,
"capacity_type": "ON_DEMAND",
"min_size": 1,
"desired_size": 2,
"max_size": 5,
"label": "ml-inference"
}
]

This configuration ensures your ML providers run on {hardware} particularly designed for his or her computational profile. By labeling these nodes, you possibly can explicitly direct your ML workloads to them.

When you’ve established your specialised infrastructure, you might want to guarantee your ML providers are configured to make use of it:

providers:
inference-api:
construct: ./model-service
port: 8080
well being: /well being
nodeSelectorLabels:
convox.io/label: ml-inference
scale:
depend: 1-5
targets:
cpu: 60

This configuration ties your inference service to your specialised infrastructure and units up clever autoscaling based mostly on precise utilization.

When correctly applied, these optimizations ship important advantages. In a single case research, a monetary providers firm implementing these methods for his or her fraud detection mannequin achieved:

• 73% discount in inference latency (from 230ms to 62ms)
• 40% lower in infrastructure prices
• Elimination of useful resource competition between ML and net providers
• Simplified operations for his or her knowledge science staff

For much more optimized ML deployments, contemplate these extra methods:

ML mannequin compilation will be resource-intensive. Through the use of devoted construct infrastructure, you possibly can optimize this course of with out impacting manufacturing workloads:

$ convox apps params set BuildLabels=convox.io/label=ml-build BuildCpu=2048 BuildMem=8192 -a model-api

ML workloads usually have particular reminiscence necessities. You possibly can outline exact limits on the service degree:

providers:
inference-api:
# ... different configuration
scale:
restrict:
reminiscence: 16384  # 16GB RAM restrict
cpu: 4000      # 4 vCPU restrict

Whereas all these optimizations are attainable with uncooked Kubernetes, implementing them requires important experience in container orchestration, cloud infrastructure, and ML operations.

Utilizing a platform method dramatically simplifies this course of, permitting engineering groups to concentrate on their fashions quite than the infrastructure complexities.

Optimizing ML inference workloads doesn’t need to imply diving deep into Kubernetes complexities or constructing a devoted MLOps staff. With the proper method to workload placement and infrastructure configuration, groups can obtain important efficiency enhancements and price reductions whereas sustaining operational simplicity.