Platform Engineering for AI/ML Teams: Building the Foundation

Most AI/ML teams operate with infrastructure that was built ad hoc — a collection of Jupyter notebooks, hand-configured GPU instances, bespoke deployment scripts, and tribal knowledge about which S3 bucket holds which model artifacts. This works when you have two data scientists. It breaks catastrophically when you have twenty.

Platform engineering offers a way out. By applying the same principles that have transformed application development — self-service interfaces, golden paths, automated guardrails — to the ML lifecycle, organizations can dramatically reduce the friction between model development and production deployment.

Why AI/ML Teams Need Platform Engineering

The ML lifecycle has unique infrastructure requirements that traditional DevOps platforms do not address well. Data scientists need GPU compute on demand, but they also need experiment tracking, dataset versioning, feature stores, model registries, and serving infrastructure. Each of these components has its own operational complexity, and most ML teams lack the infrastructure expertise to manage them reliably.

The result is predictable: data scientists spend 40-60% of their time on infrastructure tasks rather than model development. They become reluctant to deploy models because the process is manual and fragile. They hoard GPU instances because releasing them means waiting days to get new ones provisioned. Technical debt accumulates in notebooks that were never designed to be production systems.

A platform engineering approach inverts this dynamic. The platform team builds and maintains the infrastructure, and the ML team consumes it through well-defined interfaces. The data scientist's job becomes writing and improving models, not managing Kubernetes manifests.

The Golden Path Concept for ML Workflows

A golden path is an opinionated, well-supported workflow that handles the most common use case with minimal friction. For ML teams, the golden path typically covers four stages: experimentation, training, evaluation, and deployment.

The experimentation stage provides a self-service environment where data scientists can spin up GPU-enabled workspaces with pre-configured dependencies. This might be a JupyterHub instance backed by Kubernetes, or a set of VS Code remote development environments with GPU access. The key requirement is that the environment is reproducible and disposable — any data scientist should be able to get a working environment in under five minutes.

The training stage provides a job submission interface. Data scientists define their training configuration (model architecture, hyperparameters, dataset reference, compute requirements) in a structured format, and the platform handles scheduling, resource allocation, checkpointing, and artifact storage. The interface might be a CLI tool, a web UI, or a Python SDK — what matters is that the data scientist never interacts directly with the underlying orchestration layer.

The evaluation stage runs automated quality checks against trained models. This includes standard metrics (accuracy, latency, throughput) but also organization-specific checks like bias detection, data drift analysis, and compliance validation. Failed checks block deployment automatically.

The deployment stage promotes validated models to serving infrastructure. A single command or merge-to-main trigger should be sufficient to deploy a model to a canary environment, run integration tests, and roll out to production with traffic shifting.

Key Platform Components

Building an ML platform requires assembling and integrating several infrastructure components. The specific tools matter less than the integration quality between them.

Compute Provisioning is the foundation. Kubernetes with GPU support (NVIDIA device plugin, node auto-scaling via Karpenter or Cluster Autoscaler) provides the flexibility to run both training and inference workloads. The platform should abstract away node selection, GPU type mapping, and resource quotas behind a simple request interface.

Experiment Tracking tools like MLflow, Weights & Biases, or Neptune provide the lineage between code, data, hyperparameters, and results. The platform should configure these tools to capture experiment metadata automatically, so data scientists do not need to add instrumentation manually.

Model Serving infrastructure handles the operational complexity of running models in production. Tools like Seldon Core, KServe, or NVIDIA Triton provide standardized serving interfaces, auto-scaling, A/B testing, and canary deployments. The platform wraps these in a deployment interface that requires only a model artifact reference and a resource profile.

Data Pipelines connect feature engineering, training data preparation, and batch inference. Tools like Apache Airflow, Dagster, or Prefect provide workflow orchestration, while the platform provides pre-built connectors for common data sources and standardized data formats.

Observability for ML workloads extends beyond traditional APM. The platform needs to monitor model-specific metrics (prediction distributions, feature drift, latency per model version) alongside infrastructure metrics (GPU utilization, memory pressure, queue depth).

Self-Service Through Internal Developer Platforms

The highest-leverage investment a platform team can make is in self-service interfaces. Every manual infrastructure request — "I need a GPU instance," "please deploy this model," "can someone check why my training job failed" — is a signal that the platform has a gap.

Effective self-service for ML teams typically includes a service catalog (available compute profiles, model serving templates, pipeline templates), a CLI and web interface for common operations, automated provisioning with appropriate guardrails (cost limits, resource quotas, security policies), and comprehensive documentation that is integrated into the tools themselves.

The goal is not to eliminate the platform team's involvement entirely. It is to shift their work from ticket-driven operations to capability-building. When the platform team spends their time improving the platform rather than handling individual requests, the entire organization accelerates.

Getting Started

If your organization is early in this journey, start with the highest-friction pain point. For most ML teams, that is either compute provisioning (getting GPU access) or model deployment (getting models into production). Solve one of these problems well before expanding to the full platform vision.

Build for the 80% case. The golden path should handle the most common workflow elegantly. Edge cases can be handled with escape hatches that give teams direct infrastructure access when needed, but the default experience should be simple and opinionated.

Measure platform adoption the same way you would measure a product: usage, time-to-value, and user satisfaction. A platform that nobody uses is worse than no platform at all.