AI Weather Prediction Pipeline: From User Message to Accurate Forecast

Today we’re going to unleash the power of AI to predict weather forecasts! In this article I’ll walk through how to build a robust pipeline that takes user messages and turns them into accurate weather predictions. But keep in mind — this isn’t just about bots; it’s about harnessing the collective intelligence of different models to accomplish a task!

“Everybody remain calm: this is an AI takeover. Also, 75% chance of showers this weekend. Stay safe.”

First up — There’s Currently Not a Yes/No Binary Model

I mean, it would be so tempting to just go,

response = model({
    "question": "Is this person asking about the weather forecast?",
    "context": "Is it going to snow this week in New York?",
}) # outputs => "yes", or even better `True`

However, there’s no such thing. I suppose since human text is so nuanced it would be dangerous for an AI model to make a binary determination like that.

So, we have to think outside the box on this one.

Perhaps we can detect the topic from the question. If it’s something like “weather_forecast” we…