How do I implement IT infrastructure for AI/ML pipelines?

Building infrastructure for AI/ML pipelines means designing an environment that handles data processing, model training, inference, and storage without becoming a bottleneck. Here’s how to approach it.

1. Assess Requirements First

Identify the actual workloads: training, inference, batch processing, real-time prediction. Each has different compute and latency profiles.
Define scale. A prototype serving five users needs different infrastructure than a production pipeline retraining nightly on petabytes.
Talk to data scientists, developers, and business stakeholders early. Their SLAs, data gravity, and tooling preferences will drive your decisions.

2. Choose Compute That Matches the Workload

AI/ML workloads are compute-bound. Match hardware to the job.

CPU vs GPU

CPUs handle lightweight preprocessing, traditional ML (scikit-learn, XGBoost), and inference for smaller models.
GPUs are mandatory for deep learning (TensorFlow, PyTorch) where parallel processing cuts training time from days to hours.

GPU Selection for Training

Production training: NVIDIA A100, H100, or AMD Instinct MI250/MI300. These offer tensor cores, high VRAM, and NVLink/Infinity Fabric for multi-GPU scaling.
NVIDIA DGX systems integrate GPUs, networking, and software stack — expensive but validated.
Consumer cards (RTX 3090/4090) work for development workstations but lack ECC memory, driver support for virtualization, and 24/7 reliability.

Distributed Training

For models that don’t fit on one GPU, deploy multiple servers with high-speed interconnects: NVLink/NVSwitch inside a node, InfiniBand or RoCE (RDMA over Converged Ethernet) between nodes.
Size the fabric for your model parallelism strategy (data parallel, tensor parallel, pipeline parallel).

3. Storage That Feeds the GPUs

Starved GPUs are expensive idle hardware. Storage must sustain high throughput and IOPS.

Storage Tiers

Hot tier (training data, checkpoints): NVMe over Fabrics (NVMe-oF), parallel filesystems (WEKA, VAST, BeeGFS, Lustre), or local NVMe with caching.
Warm tier (datasets, model artifacts): Object storage — S3 on-prem (MinIO, Ceph RGW) or cloud (S3, GCS, Azure Blob).
Cold tier (archival, compliance): Erasure-coded object storage or tape.

Design Rules

Target 10–20 GB/s per GPU server for training workloads. Benchmark with your actual data access patterns (random vs sequential, small files vs large).
Use data tiering: keep active datasets on fast storage, age out unused data automatically.
Shared namespace across compute nodes avoids data duplication and copy jobs.

4. Networking: Don’t Let the Fabric Be the Limit

Training clusters: 100/200/400 GbE or InfiniBand (HDR/NDR). Use RoCE v2 if you need Ethernet compatibility.
Inference serving: 25/100 GbE typically sufficient, but prioritize low latency and consistent tail latency.
Separate storage, management, and compute traffic with VLANs or VRFs. Congestion on the management plane shouldn’t stall training.
In Kubernetes, use CNI plugins that support RDMA (e.g., SR-IOV, Multus with RDMA device plugin) for pod-to-pod GPU communication.

5. Virtualization and Containerization

VMs add overhead (hypervisor tax, GPU passthrough complexity). Use only when you need hard multi-tenancy or legacy OS support.
Containers are standard. Docker for packaging; Kubernetes for orchestration.
Kubernetes operators (NVIDIA GPU Operator, KubeFlow) automate GPU device plugin, driver containers, MIG partitioning, and scheduler extensions.
For batch training, consider Kubernetes with Volcano or Kueue for gang scheduling and queue management.

6. Software Stack

Frameworks and Libraries

Standardize on PyTorch or TensorFlow. Pin versions per project; use container images (NVIDIA NGC, upstream Docker Hub) for reproducibility.
Install CUDA, cuDNN, NCCL, and drivers matching your GPU generation. Mismatched versions cause silent performance regressions.
For traditional ML: scikit-learn, XGBoost, LightGBM — often CPU-only and lighter to deploy.

Data Processing

Apache Spark for large-scale ETL and feature engineering. Run on Kubernetes (Spark Operator) or dedicated YARN/Standalone clusters.
Pandas/Dask for single-node or moderate-scale preprocessing. Dask scales to clusters with familiar APIs.

Model Serving

TensorFlow Serving, TorchServe, Triton Inference Server, or vLLM for LLMs. Triton supports model pipelines, dynamic batching, and multiple frameworks simultaneously.
Deploy behind a load balancer with health checks; autoscale on request latency or queue depth.

7. Backup and Data Protection

Datasets and model checkpoints are intellectual property. Treat them like source code.

Backup

Snapshot hot storage daily; replicate to a second site or cloud. Tools: Velero for Kubernetes resources, CSI snapshots for PVs, or storage-native replication.
Version datasets and models with DVC or Git-LFS. Store metadata (hyperparameters, metrics, data lineage) in MLflow or a feature store.

Disaster Recovery

Define RPO/RTO per workload. Training can tolerate hours of data loss; inference serving may need minutes.
Test restores quarterly. A backup you haven’t restored is a wish.

Security

Encrypt data at rest (storage encryption, LUKS, cloud KMS) and in transit (TLS 1.3, mTLS between services).
RBAC on Kubernetes, storage, and model registry. Least privilege for data scientists — they shouldn’t need cluster-admin.
Scan container images for vulnerabilities (Trivy, Grype) in CI/CD.

8. Monitoring and Optimization

What to Watch

GPU utilization, memory usage, temperature, power draw (DCGM exporter → Prometheus).
Storage throughput, latency, capacity, and quota usage.
Network throughput, packet drops, RDMA error counters.
Kubernetes: pod status, scheduler queue depth, resource quotas.

Tooling

Prometheus + Grafana for metrics; Loki or Elasticsearch for logs; Tempo/Jaeger for traces.
NVIDIA DCGM, node-exporter, kube-state-metrics, cAdvisor.
Alert on sustained low GPU utilization during training jobs — it usually means data loading bottlenecks or poor batch sizing.

Optimization Loop

Profile training jobs (NVIDIA Nsight Systems, PyTorch Profiler) to find stalls.
Right-size GPU allocations: use MIG (Multi-Instance GPU) on A100/H100 to run multiple small inference workloads on one physical GPU.
Autoscale node pools based on pending pods and queue depth, not just CPU utilization.

9. Cloud, On-Premises, or Hybrid

Cloud

Managed services (SageMaker, Vertex AI, Azure ML) reduce ops burden but increase cost and lock-in.
GPU instances (AWS P4d/P5, Azure NDv4/NDv5, GCP A3) are available on demand — good for burst workloads.
Watch egress fees and data gravity. Moving petabytes to cloud for training once may be cheaper than storing there permanently.

On-Premises

Capital expense upfront, lower per-GPU-hour cost at high utilization (>60%).
Required for data sovereignty, regulatory compliance, or latency-sensitive inference at the edge.
You own the hardware lifecycle: power, cooling, rack space, drive failures, firmware updates.

Hybrid

Train on-prem where data lives; burst to cloud for hyperparameter sweeps or seasonal peaks.
Use consistent Kubernetes (Rancher, OpenShift, Anthos) and storage (Portworx, Longhorn, cloud-native CSI) across environments.
Replicate model artifacts to cloud registry for global inference deployment.

10. MLOps Tooling

Pipeline orchestration: Kubeflow Pipelines, Airflow, or Prefect. Choose based on team familiarity and Kubernetes integration needs.
Experiment tracking: MLflow (self-hosted), Weights & Biases, or Neptune. Log parameters, metrics, artifacts, and code version.
Model registry: MLflow, Neptune, or cloud-managed. Enforce promotion gates (staging → production) with automated tests.
Feature store: Feast, Tecton, or Hopsworks for reusable, versioned features across training and serving.