Thread by @paulabartabajo_

Wanna learn MLOps?

Stop reading blog posts.
Build a prediction service instead 🚀

Here is a project you can build (for free) 👩🏽‍💻👨‍💻↓↓↓

Let's build a Machine Learning service to predict the Air Quality Index (AQI) in your city in the next 3 days, using a 100% serverless stack.

You will learn a lot, AND you will build something useful for society.

Win-win 🏆🏆

These are steps to build this ↓

Step 1: Feature generation script 🐍

1 → fetches raw weather and pollutant data from an external API like

2 → computes features from this raw data (aka model inputs), and targets (aka model outputs)

3 → stores these features in the *Feature Store* https://t.co/72uTTBYnqFaqicn.org/city/barcelona

Step 2: Backfill historical (features, targets) ⏮️

To train a Machine Learning model later, you need enough historical data (features, targets) in your Feature Store.

Run the feature script for a range of past dates, to get enough training data.

Step 4: Model training script 🏋️

1 → fetches historical (features, targets) from the Feature Store.

2 → trains and evaluate the best ML model possible for this data, e.g. XGBoostRegressor.

3 → stores the trained model in the Model Registry.

Step 5: Automate execution of the feature script 🕰️

Create a GitHub action to automatically run the feature script (from step 1) every hour.

GitHub actions are serverless computing power to run your code on a schedule. For free.

Beautiful.

Step 6: Create a web app to show model predictions 👨🏽‍💻

Streamlit is a powerful Python library to develop and deploy web data apps.

Your app

1 → loads the model and features from the *Feature Store*,

2 → computes model predictions and shows them on a beautiful UI.

BOOM!

Bonus 🎁

You can create another GitHub action to automate the model training script.

Why re-train the model? 🤔

Because ML model performance decreases over time.
The best way to mitigate this is to regularly re-train the model, like once a week.

Wanna level up in ML/MLOps?

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