Platform Engineering for AI: Building the Golden Path for ML Teams

TL;DR

The explosion of AI has exposed a massive gap in how we build platforms. We’ve spent a decade perfecting the IDP for microservices, but ML teams are still stuck in the “Wild West” of manual deployments and bespoke pipelines. To scale AI, platform teams must build a Golden Path that treats models as first-class citizens, abstracts away GPU complexity, and automates the journey from notebook to production.

The collision between ML workflows and platform engineering

I’ve spent over 20 years building platforms, from the early days of virtualization to the cloud-native revolution. At State Street and later with our clients at aimtheory, I’ve seen the same pattern repeat: a new workload arrives, and the existing platform team tries to shove it into their existing CI/CD pipelines. It worked for Java monoliths. It worked for Go microservices. It is failing miserably for Machine Learning.

The fundamental disconnect is that ML is not deterministic. In traditional software, if the code passes tests and the build is green, the artifact is ready. In ML, you can have perfect code and still produce a garbage model because the data drifted or the hyperparameter tuning failed. Traditional CI/CD tools expect a fast, linear build. ML experiments can run for days, require terabytes of data, and need specialized hardware that traditional Jenkins or GitHub Actions runners can’t provide without massive customization.

Then there’s the “GPU scheduling hell.” Traditional schedulers think in terms of CPU and RAM. When an ML team needs eight A100s for a training job, they shouldn’t be writing custom Kubernetes manifests or ticketing an infra team. If your platform doesn’t handle hardware acceleration as a native resource, your data scientists will bypass you and build shadow infrastructure in a separate cloud account. I’ve seen this happen at scale, and the result is always the same: a fragmented, unmanaged mess that costs millions and delivers zero production value.

The gap between a Jupyter notebook and a production inference service is where most AI projects go to die. Data scientists are brilliant at math and modeling, but they shouldn’t have to be experts in Kubernetes ingress, sidecar injection, or mTLS. When we force them to handle the “plumbing,” we create a bottleneck. The goal of platform engineering for AI is to bridge this gap by providing a paved road—a Golden Path—that lets the ML team focus on the model while the platform handles the rest.

What a “golden path” for AI/ML looks like

A Golden Path isn’t just a set of tools; it’s a shift in mindset. We need to apply the same IDP concepts we use for developers to our ML practitioners, similar to the principles in Internal Developer Platforms: Build the Right Thing. This means providing self-service capabilities that abstract away the underlying infrastructure. A data scientist should be able to click a button (or run a CLI command) to spin up a training environment, register a model, and deploy it to a staging cluster.

In our work at aimtheory, we define the ML Golden Path as a series of integrated, standardized stages. It starts with a standardized experimentation environment—think managed JupyterHub or VS Code Server instances with pre-configured drivers and libraries. No more “it works on my machine” because someone has a different version of CUDA installed. The platform provides the environment, and the environment is identical to the production execution context.

The next stage is the transition from experiment to pipeline. The Golden Path provides templates for training jobs that automatically hook into experiment tracking services. When a model is “finished,” it isn’t a file on a laptop; it’s an entry in a model registry with full lineage back to the code, data version, and training parameters. This is the “deterministic build” equivalent for ML.

Finally, the deployment phase must be standardized. We shouldn’t be writing new Flask or FastAPI wrappers for every model. The platform should provide a standard inference server (like vLLM or Seldon Core) that takes a model from the registry and wraps it in a production-ready container with built-in monitoring, scaling, and security. This is where the IDP concept really shines: the ML team provides the “what” (the model), and the platform provides the “how” (the serving infrastructure).

“If your data scientists are writing YAML to deploy a model, you haven’t built a platform—you’ve built a hurdle.”

Infrastructure patterns

To build this Golden Path, we need to move beyond generic Kubernetes clusters and start thinking about specialized ML infrastructure patterns. This isn’t just about “installing KubeFlow” and calling it a day. It’s about integrating a set of core services that work together seamlessly.

GPU Scheduling and Orchestration

The foundation of any AI platform is how it handles compute. We’re moving away from static GPU assignment. You need a scheduler that understands resource sharing and preemption. Tools like Ray or Volcano (on top of Kubernetes) allow for more sophisticated scheduling of distributed training jobs. At the platform level, this means implementing things like Time-Slicing or Multi-Instance GPU (MIG) to ensure you aren’t wasting expensive compute cycles on small tasks.

The Model Registry

Think of the Model Registry as your Artifactory or Docker Hub for ML. It’s the single source of truth. We frequently use MLflow or BentoML for this. The registry must capture more than just weights; it needs metadata, signatures for security, and performance benchmarks. The Golden Path mandates that nothing goes to production unless it comes from the registry and has been “promoted” through the proper lifecycle stages.

Feature Stores

Data is the biggest dependency in ML. A Feature Store (like Feast or Hopsworks) solves the “training-serving skew” problem. It provides a consistent way to access data for both training and real-time inference. For the platform engineer, the feature store is a managed data service that abstracts away the underlying databases (Redis, Snowflake, etc.), providing a simple API for the ML team to consume.

Experiment Tracking

You cannot manage what you do not measure. Every run in the Golden Path must be tracked. Tools like Weights & Biases or the tracking component of MLflow are essential here. The platform’s job is to ensure that these tools are automatically injected into the training environment so that tracking isn’t an “optional” step the data scientist has to remember to configure.

How IDP concepts apply to ML

The core tenets of an IDP—self-service, guardrails, and cognitive load reduction—apply perfectly to the ML domain, but the implementation looks different. In a traditional IDP, self-service might mean “create a new React app.” In an AI platform, it means “create a new fine-tuning job.”

Standardized inference pipelines are a great example of applying IDP patterns. Instead of every team building their own Dockerfile for a model, the platform provides a base image. This image includes the necessary drivers, security patches, and a standard wrapper for logging and metrics in the company’s preferred format (e.g., Prometheus/OpenTelemetry). The ML team just points the platform to their model artifact, and the platform handles the rest. This is exactly how we’ve seen teams at State Street reduce their time-to-market for new models from months to days.

The “guardrails” in an AI IDP are also specialized. Instead of just checking for open ports or missing labels, we’re checking for model bias, data drift, and inference latency. We can bake these checks into the “promotion” logic of our Golden Path. If a model’s accuracy on a validation set drops below a certain threshold, the automated pipeline blocks it from moving to production. This is “Policy-as-Code” for the AI era.

By reducing the cognitive load on ML teams, we allow them to do what they are actually paid for: improving model performance and solving business problems. They shouldn’t have to care about the intricacies of vLLM optimization or how to configure HPA for a GPU-bound service. The platform handles the complexity under the hood, providing a clean, high-level interface, with ownership patterns aligned to AI platform team structure best practices.

Real-world architecture

A successful AI platform is built in layers. The platform team focuses on the bottom three layers, while the ML team operates in the top layer.

graph TD
    subgraph "ML Team Workspace"
        A[Experimentation & Notebooks] --> B[Training Pipelines]
        B --> C[Model Evaluation]
    end

    subgraph "AI Internal Developer Platform"
        D[Model Registry]
        E[Feature Store]
        F[Inference Server Templates]
        G[Experiment Tracker]
    end

    subgraph "Platform Foundation"
        H[GPU/CPU Orchestration - K8s/Ray]
        I[Data Lake/Warehouse Access]
        J[Observability & Security]
    end

    C --> D
    D --> F
    F --> H
    B --> G
    B --> E
    H --> J

This layered approach ensures clear ownership. The platform team provides the Inference Server Templates and the GPU Orchestration. When the ML team completes an experiment, the platform picks up the artifact from the Model Registry and deploys it using the standardized templates. This separation of concerns is the secret to scaling.

Anti-patterns

The most common failure I see is the “Shadow ML Platform.” This happens when the central platform team is too slow or too restrictive, leading the ML team to build their own infrastructure using credit cards and unmanaged cloud accounts. This creates massive security risks and fragmented data silos. If your platform is harder to use than a raw AWS account, your teams will find a way around you.

Another dangerous anti-pattern is the “Monolithic ML Pipeline.” Teams try to build one giant Jenkins job that does everything from data ingestion to model deployment. These are impossible to maintain and fail frequently. ML workflows need to be modular and event-driven. A change in the data should trigger a retraining job, but that job should be a separate, decoupled process from the inference deployment.

Finally, we have the “Missing Metadata” trap. Many teams focus only on the model weights. They forget to track which version of the training code produced the model, what the hyperparameters were, and exactly which slice of data was used. Without this lineage, you can never truly reproduce a model or debug a production failure. A Golden Path that doesn’t enforce metadata collection is just a fancy way to lose track of your work.

At aimtheory, we often see organizations with “Duplicated Feature Engineering.” Three different teams are all calculating the same “user_spend_last_30_days” feature in three different ways. This isn’t just inefficient; it leads to inconsistent model results. This is exactly why a centralized Feature Store is a critical platform component, not just a “nice-to-have” tool.

Maturity model

Scaling your AI platform is a journey. You can’t go from manual scripts to a fully automated Golden Path overnight. We use this maturity model to help organizations benchmark their progress and plan their roadmap.

Most enterprises we talk to are stuck somewhere between Level 1 and Level 2. They have talented data scientists, but their “platform” is just a collection of Jira tickets and manual steps. Moving to Level 3 requires a dedicated platform engineering effort to build the self-service layers we’ve discussed, often using the rollout approach from Golden Paths v3: multi-runtime templates and test orchestration.

Actionable next steps

If you’re a platform lead looking to support AI workloads, don’t try to build everything at once. Start with the biggest pain point—usually the gap between the model and the production API—and track adoption with platform reliability scorecards and SLOs for shared services.

1. Standardize the Inference Wrapper: Pick a tool like vLLM or Seldon and build a “Golden Image” that your ML teams can use. Show them how much easier it is than writing their own Flask apps.
2. Centralize the Registry: If your models are currently sitting in S3 buckets named model_v2_final_FINAL.pkl, install MLflow today. Make it the only way to “promote” a model to a production environment.
3. Implement GPU Quotas: Give teams the autonomy to run their own jobs without asking for permission, but use Kubernetes resource quotas and namespaces to ensure one team doesn’t starve the entire organization.

Platform engineering for AI is the next frontier for our discipline. By building Golden Paths that respect the unique needs of ML workflows, we can turn AI from a series of expensive experiments into a repeatable, production-grade engine for the business.

Teams accelerating this journey usually pair execution with focused platform engineering services that ship.