Transforming Technical Debt into Clarity

As a consultant, my job is to solve problems that propel businesses forward. Recently, I led a Google Cloud Platform (GCP) migration project for a Machine Learning application designed for electricity trading via FinGrid on NordPool—a high-stakes system where precision, speed, and reliability are everything. The web app, accessible at ilmatarbrain.com, relied on a pretty complex infrastructure that had grown unwieldy over time. This wasn’t just a technical upgrade; it delivered measurable business value under intense constraints. In this post, I’ll first highlight the business benefits that made this project a success for stakeholders, followed by a technical deep dive for the engineers. Curious about what made the infrastructure so intricate? Read on to the technical section for the full scoop. Let’s start with the business wins.

The Business Perspective: Delivering Value Beyond the Code

The client’s ML-powered trading platform needed to migrate to a new GCP instance to keep pace with the fast-moving NordPool market. With electricity prices fluctuating by the minute, downtime or delays weren’t an option—and the schedule was razor-tight. Here’s how we turned technical debt into a strategic asset:

Beat the Clock with a Tight Schedule: Deadlines were non-negotiable, and we delivered on time. Proven strategies and streamlined workflows kept us ahead of the curve without cutting corners.
Saved Time with Reusable Modules: By reusing battle-tested modules from past projects, we slashed development time by weeks. This speed freed up resources for fine-tuning the trading algorithms.
Smooth Communication, No Hiccups: Clear, consistent updates kept tech teams, managers, and executives in sync. No surprises or delays—just seamless collaboration that built confidence.
Reduced Costs Through Efficiency: Automation and smart design minimized manual effort and future upkeep. The client saw instant savings, with a system built to scale cost-effectively.
Faster Deployments, Quicker Wins: Automated pipelines cut deployment times from days to hours. Faster updates meant the ML models could adapt to market shifts in near real-time.
Future-Proof Flexibility: The revamped setup isn’t just a fix—it’s adaptable. Configurable infrastructure lets the client pivot as trading needs evolve, no costly rewrites required.
Minimized Risk, Maximized Reliability: We tamed undocumented systems and added robust processes, slashing the risk of outages. The business gained a rock-solid foundation for 24/7 trading.
Scalability Without the Stress: The infrastructure handles growing data and transaction volumes effortlessly. More trades, more insights—no bottlenecks or budget surprises.
A Competitive Advantage: A modern, efficient cloud setup turned IT into a business driver. The client now has the agility to outmaneuver rivals in the NordPool arena.

This project wasn’t just about moving a system—it was about powering a trading edge. The tight timeline was met, costs were optimized, and the business emerged with a platform ready to dominate electricity markets. Now, let’s dive into how we made it happen—engineers, this one’s for you!

The Technical Deep Dive: Refactoring, Provisioning, and Automating

The Challenge: A Hardcoded Mess

When I joined the project, the client’s Infrastructure-as-Code (IaC) codebase was a relic—designed to manage a GCP instance but bogged down with hardcoded values like IP addresses and resource names. It was fragile, inflexible, and only partially represented the actual infrastructure, with undocumented components lurking in the GCP wild. The mission: migrate everything to a new GCP instance while modernizing the setup for reproducibility and growth—crucial for a complex system like the one powering ilmatarbrain.com/login.

This wasn’t just a migration—it was a full-on transformation.

Step 1: Refactoring the IaC

I started by refactoring the IaC from the ground up. Hardcoded values were replaced with variables, parameterized inputs, and modular designs, making the system fully configurable across development, staging, and production environments. I also added a local development setup for the IaC, allowing engineers to test changes on their machines before pushing to GCP—a small but impactful addition for rapid iteration.

To tackle the undocumented “ghost” infrastructure, I reverse-engineered those rogue components and brought them into the refactored codebase. By the end, everything lived in version control, visible and manageable.

Step 2: Provisioning the Infrastructure

Next, I provisioned the new GCP environments using Terraform, breaking the process into clear, sequential steps. Here’s what I deployed across dev, staging, and production:

Core GCP Setup: Remote buckets, service enablement, DNS (000-gcp-remote-bucket, 001-enable-gcp-services, 006-gcp-dns-subzones, etc.).
Security & Access: Automated/manual secrets, IAM (002-automatic-secrets, 002-manual-secrets, 005-iam).
Networking: VPCs, peering, static addresses, firewalls (120-vpc, 121-vpc-peering, 007-dns-static-addresses, 415-firewall-rules).
Storage & Databases: Buckets, Cloud SQL, Big Query (015-gcp-buckets-for-sites, 150-gcp-cloud-sql, 330-big-query).
Compute: VMs for APIs, web UIs, and VS Code dev setups (211-gcp-vm-ecp-edx-vscode-setup, 212-gcp-vm-api, 250-gcp-vm-wui, etc.).
CI/CD & Scheduling: Artifact registries, schedulers (017-artifact-registry, 320-scheduler).
Modular Utilities: Reusable modules (modules).

Each step was scripted in Terraform, with a local dev setup mirroring the cloud for testing. This modular approach made troubleshooting straightforward and gave the team a repeatable blueprint.

Step 3: Building CI/CD Pipelines

No modern system is complete without automation—especially for a trading app where every second counts. I built CI/CD pipelines for the client’s applications:

Node.js + Vue Frontend: Automated builds, tests, and deployments to GCP buckets for static sites.
Python FastAPI Backend: Packaged and deployed to VMs, integrated with Cloud SQL and Big Query.

These pipelines, backed by an artifact registry, streamlined deployments and made rollbacks painless. The local dev setup also tied into the pipelines, letting engineers validate changes end-to-end before hitting the cloud.

Documentation: Minimalistic and Code-Driven

Documentation was a priority—but not in the traditional sense. Instead of sprawling manuals, I kept it minimalistic: anything that could be automated into runnable code was scripted into the IaC or pipelines. For the rest, I built documentation generation into the system. Technical readers can start with a simple --help flag on the CLI entry point (e.g., oneliner --help) and explore from there. This approach ensures the codebase itself is the source of truth, with just enough human-readable context to get started.

The Technical Outcome

The result? A migrated GCP environment that’s light-years ahead of its predecessor. The IaC is now configurable, the infrastructure is fully provisioned, and the pipelines automate deployment with precision. The local dev setup empowers engineers to experiment safely, while the minimal, code-driven docs keep maintenance low—all critical for keeping ilmatarbrain.com/login humming in the NordPool market.

Key Technical Takeaways

Configurability is King: Hardcoded values are a maintenance nightmare. Parameterizing IaC pays off in flexibility.
Minimal Docs, Maximum Code: Automate everything possible into runnable scripts. Let --help guide the rest.
Local Dev Matters: A local IaC setup speeds up iteration and catches issues early.
Automation Wins: CI/CD pipelines turn chaos into predictability.

Wrapping Up

This project was a blast—solving tough problems and delivering a system that powers both business success and technical excellence. For the business, it’s a competitive edge in electricity trading. For the engineers, it’s a refactored masterpiece. Have you tackled a similar challenge? Drop your thoughts below—I’d love to hear your stories!