The 5 things we broke building our first major ML pipeline at Besttech (and how we fixed them).

The "Hello World" of Machine Learning is easy. You import Scikit-Learn, fit a model on a clean CSV, and get a nice accuracy score.

Production Machine Learning is a nightmare.

At Besttech, we recently took on a project to move a client's predictive analytics model from a chaotic set of local Jupyter Notebooks into a fully automated, cloud-native pipeline. We thought we had it mapped out. We thought we knew MLOps.

We were wrong.

We broke things. Big things. But in the process, we built a robust engine that now processes terabytes of data without flinching. Here are the 5 major failures we encountered and the engineering fixes we deployed.

1. We Broke: The Concept of Time (Data Leakage) ⏳ The Failure: Our initial model showed spectacular performance during training—98% accuracy. We were high-fiving in the Slack channel. But when we deployed it to the live environment, accuracy plummeted to 60%.

The Root Cause: We had accidentally trained the model using features that wouldn't actually exist at prediction time. We included "Total Monthly Spend" in a model designed to predict start-of-month churn. We were effectively letting the model "cheat" by seeing the future.

The Fix: We implemented a strict Feature Store (using Feast). This forced us to timestamp every feature. Now, when we create a training set, the system performs a "point-in-time correct" join, ensuring the model only sees data that was available at that specific historical moment.

1. We Broke: The Cloud Bill (Resource Hoarding) 💸 The Failure: We treated our cloud instances like our laptops. We spun up massive GPU instances for the entire duration of the pipeline—extraction, cleaning, training, and deployment.

The Root Cause: 90% of our pipeline was simple data wrangling (CPU work), yet we were paying for expensive GPUs the entire time.

The Fix: We decoupled the steps using Kubernetes containers.

Step 1 (ETL): Runs on cheap, high-memory CPU nodes.

Step 2 (Training): Spins up a GPU node, trains the model, and immediately shuts down.

Step 3 (Inference): Runs on lightweight serverless functions. Result: We cut compute costs by 65%.

1. We Broke: Python Dependencies (The "It Works on My Machine" Classic) 🐍 The Failure: The data scientist used pandas 1.3.0. The production server had pandas 1.1.5. The pipeline crashed silently because a specific function signature had changed.

The Fix: We banned manual environment setups. We moved to strict Dockerization. Every step of the pipeline now runs in its own Docker container with a frozen requirements.txt. If the container builds, the code runs. Period.

1. We Broke: Trust (Silent Failures) 🤫 The Failure: One week, the source data feed broke and started sending all "zeros" for a specific column. Our pipeline didn't crash. It happily ingested the zeros, trained a garbage model, and deployed it. The client started getting nonsensical predictions.

The Root Cause: We were testing for code errors, not data errors.

The Fix: We introduced Data Expectations (using Great Expectations) at the ingestion layer.

Check: Is column age between 18 and 100?

Check: Is transaction_value non-negative?

Check: Is null count < 5%? If the data violates these rules, the pipeline halts immediately and alerts the Besttech Slack channel before any damage is done.

1. We Broke: The Feedback Loop (Model Drift) 📉 The Failure: We deployed the model and moved on to the next project. Three months later, the client called: "The predictions are getting worse every week."

The Root Cause: The market had changed. The patterns the model learned 90 days ago were no longer relevant. We had built a "static" solution for a dynamic world.

The Fix: We automated the retraining loop. We now monitor Drift Metrics (using tools like Evidently AI). If the statistical distribution of the live data deviates from the training data by more than a threshold, it automatically triggers a new training run. The pipeline is now self-healing.

The Takeaway
Building models is science. Building pipelines is engineering.

At Besttech, we bridge that gap. We don't just hand you a notebook and wish you luck; we build the messy, complex, unglamorous infrastructure that keeps your intelligence running.

Devs, be honest: Have you ever deployed a model that accidentally "cheated" by looking at future data? Tell me your worst data war story in the comments. 👇