In the rapidly evolving landscape of AI and machine learning, effectively deploying and scaling inference workloads represents a significant challenge for development teams. Many organizations find themselves caught in a dilemma: they need Kubernetes for its autoscaling capabilities and resource management, but struggle with the operational complexity it introduces.

Today, we're excited to explore how Convox's workload placement features make it possible to deploy and manage specialized AI/ML workloads without the need for Kubernetes expertise.

The Challenge of ML Workloads

Modern machine learning deployments present unique infrastructure challenges:

• Specialized hardware requirements for computation-intensive workloads
• Varying workload patterns with unpredictable scaling needs
• High resource costs that demand optimization
• Isolation needs to prevent resource contention with other applications

Traditionally, solving these challenges required specialized DevOps knowledge and complex Kubernetes configurations. But what if there was a better way?

Introducing Convox's Workload Placement

Convox provides powerful workload placement capabilities that allow you to create dedicated infrastructure for your specialized workloads, including AI/ML inference services.

At its core, this feature enables:

1. Custom node groups with specific hardware profiles to match your application needs
2. Granular control over where workloads run within your cluster
3. Isolation between build processes and production workloads
4. Optimized resource allocation based on workload requirements

Let's see how this works in practice for an ML inference API.

Setting Up Infrastructure for ML Inference

Step 1: Configure a Specialized Node Group

First, we'll define an optimized node group in our Convox rack by setting the additional_node_groups_config parameter:

$ convox rack params set additional_node_groups_config=/path/to/ml-nodes.json -r production

Where ml-nodes.json contains:

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

This configuration creates a dedicated node group using AWS's GPU-optimized instance types (g4dn.xlarge), ensuring our ML inference workloads have access to the computational resources they need for optimal performance.

Step 2: Configure Your ML Service

In your convox.yml, define your ML inference service with appropriate resource requirements:

services:
  inference-api:
    build: ./model-service
    port: 8080
    health: /health
    nodeSelectorLabels:
      convox.io/label: ml-inference
    scale:
      count: 1-5
      targets:
        cpu: 60

This configuration ensures your inference service runs exclusively on the specialized nodes we defined earlier, with appropriate autoscaling based on CPU utilization.

Real-World Performance Improvements

When one of our FinTech customers implemented this approach for their fraud detection model, they saw remarkable improvements:

• 73% reduction in inference latency (from 230ms to 62ms)
• 40% decrease in infrastructure costs by right-sizing nodes for ML workloads
• Elimination of resource contention between ML and web services
• Simplified operations for their data science team

The ability to precisely define infrastructure requirements while maintaining Convox's operational simplicity proved to be a game-changer.

Going Beyond: Fine-Tuning Your ML Infrastructure

For even more specialized needs, Convox offers several additional capabilities:

Build Optimization for ML Models

ML models often require significant resources during the build process. You can define dedicated build infrastructure with:

$ convox rack params set additional_build_groups_config=/path/to/build-nodes.json -r production

And in your application:

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

This ensures that resource-intensive model compilation happens on appropriate infrastructure without affecting your production services.

Custom Resource Limits

For ML services with specific memory requirements, you can set limits at the service level:

services:
  inference-api:
    # ... other configuration
    scale:
      limit:
        memory: 16384  # 16GB RAM limit
        cpu: 4000      # 4 vCPU limit

Getting Started Today

Ready to optimize your ML workloads with Convox? Here's how to get started:

1. Configure a runtime integration for AWS
2. Install a Convox rack with your preferred settings
3. Define your node groups using the parameters outlined above
4. Deploy your application and enjoy the performance benefits

Looking for more guidance? Check out our Convox Academy playlist for step-by-step videos on getting started.

If you're running ML workloads and want to simplify your operations while improving performance, Get Started Free or email sales@convox.com for a personalized consultation.

By combining the simplicity of Convox with the power of custom node groups and workload placement, you can now deploy sophisticated ML infrastructure without the operational overhead typically associated with Kubernetes. This means your data scientists and ML engineers can focus on building great models, not managing infrastructure.

In our next post, we'll explore how to implement cost optimization strategies for multi-environment deployments – stay tuned!