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AI Infrastructure Stack: How to Build Your MLOps Pipeline

Deploybase · August 5, 2025 · AI Infrastructure

Contents

Core Components of AI Infrastructure

Production MLOps needs: GPU compute, inference frameworks, data pipelines, orchestration, monitoring.

Each does one thing. GPUs for training. Inference frameworks for latency. Data systems for preprocessing. Orchestration for job coordination. Monitoring for visibility.

Separate training from serving. Training updates models on schedule. Serving handles continuous requests.

Slow data pipeline bottlenecks fast GPUs. Bad monitoring makes debugging hell. Architecture design matters.

GPU Compute Layer

The GPU compute layer handles model training and experimentation. This layer requires flexibility to support diverse workloads from small research prototypes to production-scale training runs.

GPU pricing guides help handle provider selection. RunPod provides cost-effective on-demand and spot pricing for variable workloads. Lambda specializes in managed infrastructure with strong support. CoreWeave targets production deployments with sustainability focus.

Provider selection depends on project needs. Research teams favor RunPod's broad hardware selection and low cost. Production teams often choose Lambda or CoreWeave for professional support and uptime guarantees.

Multi-GPU orchestration requires attention. Kubernetes-based systems like Kubeflow or Ray can coordinate training across GPU clusters. Simpler projects use provider-native orchestration tools.

Data center location affects training speed for data-intensive workloads. Collocating storage and compute reduces transfer latency. Cloud-agnostic teams often place training close to data sources.

Model Serving and Inference

Inference infrastructure must handle production demands like low latency, high availability, and cost efficiency. Several frameworks optimize this task.

vLLM excels at batching inference requests into high-throughput operations. The framework serves LLMs efficiently through sophisticated kernel optimizations. vLLM integrates with OpenAI API interfaces for drop-in compatibility with applications.

HuggingFace Text Generation Inference provides similar functionality with flexibility across model architectures. TGI supports vision models, multimodal systems, and specialized formats beyond standard transformers.

Ollama simplifies local and cloud inference with containerized models. The system handles GPU management automatically and provides OpenAI-compatible APIs.

For non-LLM models, TensorFlow Serving and TorchServe remain industry standards. These systems handle versioning, A/B testing, and traffic routing transparently.

Load balancing becomes critical as request volume scales. Round-robin systems distribute load simply. More sophisticated approaches use response time or queue depth for smarter routing.

Data Pipeline and Storage

Data handling determines end-to-end pipeline throughput. Models train only as fast as data can be loaded and processed. Efficient data pipelines prevent GPU underutilization during training.

Preprocessing steps should execute on CPU to avoid wasting GPU compute. Data augmentation, normalization, and feature extraction all run more cost-effectively on standard compute.

Storage systems require different characteristics for different access patterns. Training data benefits from sequential access patterns typical of HDD storage. Inference latency-critical workloads favor SSD or NVMe storage.

Data versioning tracks which datasets produced which models. Systems like DVC (Data Version Control) enable reproducibility and collaboration on datasets.

Feature stores centralize feature computation and serve pre-computed features to models. Feast or Tecton manage feature pipelines, versioning, and real-time serving.

Monitoring and Operations

Production ML systems require extensive monitoring beyond traditional application metrics. Model performance divergence indicates data drift or distribution shift requiring intervention.

Standard system metrics track GPU utilization, memory usage, and temperature. Chronically underutilized GPUs signal optimization opportunities. High GPU memory pressure indicates data loading bottlenecks.

Model-specific metrics track prediction quality. Classification models monitor precision, recall, and F1 score continuously. Regression models track prediction error distributions. Drift detection flags when model behavior changes unexpectedly.

Application metrics track inference latency, throughput, and error rates. SLA compliance requires monitoring percentile latencies (p95, p99) rather than averages.

Alerting systems notify operators of anomalies. Automated remediation can trigger model retraining when drift exceeds thresholds.

Logging and tracing enable rapid issue diagnosis. Structured logs capture request-response pairs for debugging. Distributed tracing follows requests through multiple system components.

FAQ

Should startups use managed platforms or build custom infrastructure? Startups typically benefit from managed platforms initially to reduce operational overhead. Custom infrastructure makes sense at scale when operational costs justify engineering investment.

How much GPU compute capacity should production systems maintain? Production systems typically maintain 50 to 100 percent headroom above peak load. During failure scenarios or traffic spikes, excess capacity prevents cascade failures.

What is the typical cost split between compute, storage, and networking? GPU compute dominates for training-heavy workloads, typically 60 to 80 percent of costs. Serving-focused workloads may shift toward storage and networking, especially for large model serving.

How often should models be retrained in production? Retraining frequency depends on data and model drift. Some systems retrain weekly, others monthly or seasonally. Data monitoring guides retraining decisions.

Can open-source frameworks replace commercial ML platforms? Yes, open-source tools increasingly provide feature parity with commercial platforms. Trade-offs involve maintenance burden, expertise requirements, and support availability.

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