This is the second post in a two-part series going through my “when to use machine learning” flowchart:

The first post focused on two things:

Wouldn’t it be cool if we could train a machine learning model to predict machine performance? In this post, we’ll look at a linear regression model I built using BQML to predict the performance of a machine given hardware and software specifications.

All of the work I do is software…

I’m constantly fascinated by machine learning and always excited to find new projects for it. But as trendy as ML has become, sometimes a SQL query or IF statement can accomplish the same job as an ML model in much less time. …

I’m always looking for new datasets for ML projects, so I was particularly excited to discover this public domain dataset of ~400k congressional bills. The dataset has 20+ data points for each bill. Here’s an example a subset of this data for one bill:

Want to build an ML model but don’t have enough training data? In this post I’ll show you how I built an ML pipeline that gathers labeled, crowdsourced training data, uploads it to an AutoML dataset, and then trains a model. I’ll be showing an image classification model using AutoML…

If you haven’t heard about AutoML yet, it‘s the newest ML offering on Google Cloud and lets you build custom ML models trained on your own data — no model code required. It’s currently available for images, text, and translation models. …

Did you miss the AutoML announcements and demos during the Cloud Next ’18 keynote? I‘ve got you covered! In this post I’ll provide an overview of the AutoML products launched and the demos I showed during the keynote. …

Can you put a dollar value on “elegant, fine tannins,” “ripe aromas of cassis,” or “dense and toasty”? It turns out a machine learning model can. In this post I’ll explain how I built a wide and deep network using Keras (tf.keras) to predict the price of wine from its…

Note: as of this writing there is no official TensorFlow library for Swift, I used Swift to build the client app for prediction requests against my model. This may change in the future, but Taylor has the final say on that.

Here’s what we’re building:

Recently I’ve been using the Google Cloud Machine Learning APIs with Node.js and Python, but I wondered — wouldn’t it be cool if there was an easy way to add them to a mobile app? That’s where the magic of Firebase comes in. I built an iOS app in Swift…

Sara Robinson

Connoisseur of code, country music, and homemade ice cream. Helping developers build awesome apps @googlecloud. Opinions = my own, not that of my company.

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