Rebuilding Our MLOps Foundation for Faster, Safer Forecasting

Project Overview

Building breakthrough forecasting models is only half the work. The other half is operational: keeping models deployable, maintainable, and comparable as they evolve. Our research velocity kept climbing, yet the path from notebook to production became fragile. This case study covers how we rebuilt that path so reliability and experimentation could reinforce one another.

Why We Needed a New Foundation

As forecasting work scaled across solar, wind, and pricing initiatives, four pressures converged. Data scientists needed freedom to iterate, engineering needed reproducible artefacts, pipelines needed a consistent interface, and the business needed measurable uplifts deployed safely. Our tooling, however, had grown organically. Every model carried slightly different runtimes, packaging rules, and implicit knowledge—meaning a “small” experiment required pipeline surgery.

Our goal became simple: make experimentation fast, and deployment boring.

The Turning Point: A Shared Model Contract

We introduced a Model Contract—an explicit definition of what every model must provide. The contract sits inside the artifact, so when a model loads, its expectations travel with it. Pipelines stop relying on tribal knowledge; the model describes itself.

Standardising Packaging with MLflow

Next, we adopted MLflow’s `pyfunc` interface as the universal packaging format. Every artifact now carries the contract, model weights, dependencies, inference logic, and lineage metadata. The exact same bundle is used for local experiments, challenger evaluation, CI/CD testing, and production inference.

Introducing the Challenger vs. Champion Loop

Forecasting gains are rarely obvious in a single notebook run. Different weather regimes, asset classes, and time horizons can flip a “better” model into a regression. We implemented a rigorous challenger-vs-champion process so only consistently superior challengers advance.

1. Champion snapshot pinned from the production registry.
2. Any new candidate registers as a challenger with its contract + metadata.
3. Both run on identical data slices through the same inference pipelines.
4. Metrics compare accuracy, stability, edge-case handling, and runtime cost.
5. Promotion requires the challenger to outperform the champion across multiple weeks.

Because every model shares the contract + MLflow format, we can compare older architectures vs. newer ones, different feature sets, alternate weather providers, and even physics-informed hybrids—without rewriting code or pipelines.

What This Enabled

The Result

We now operate on an MLOps foundation that supports rapid renewable forecasting research without compromising reliability. Models can evolve dramatically while the system around them stays stable. The same scaffolding powers ongoing initiatives—from advanced physics integrations to multi-model ensembles—ensuring innovation and operational safety move in lockstep.