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A Step-by-Step Guide to Training a Model on Google Cloud’s Vertex AI
Starting from scratch, arriving at the first model trained on Vertex AI
Vertex AI Tutorial Series
- A Step-by-Step Guide to Training a Model on Google Cloud’s Vertex AI (this article)
- A Step-by-Step Guide to Tuning a Model on Google Cloud’s Vertex AI
- How To Operationalize a Model on Google Cloud’s Vertex AI
- How To Use AutoML on Google Cloud’s Vertex AI
- How To Use BigQuery ML on Google Cloud’s Vertex AI
- How to Use Pipeline on Google Cloud’s Vertex AI
Background and Motivation
Google recently announced the general availability of its cloud platform for machine learning — Vertex AI. I’m very excited about this. I’ve long wanted to see a coherent, end-to-end story on ML workflows on Google Cloud. Over the years, Google Cloud has had many ML-related services and tools. Hopefully, this time they can unify them into one integrated platform.
With high expectations, I went to check out the documentation. I have to say, it could use a bit of improvement. There’s a lot of content and much of it is very good, but the organization is chaotic. It feels like a big collection of articles linked by a few last-minute “index-style” one-pagers. It’s hard to find out where to start. On top of that, you can’t tell which of those links are critical, so you end up getting distracted visiting all of them.
I really like the underlying services, I want to learn to use them better and I want to evangelise for them. That’s why I decided to create a series of articles that demonstrate an end-to-end tutorial of Vertex AI. It assumes no prior experience of Google Cloud’s ML services. I also try my best to contain everything in the articles so that you don’t need to jump around between the links. Obviously, I’ll include appropriate links for the interested readers to find out more details if desired.
This is the first episode of this series. We start from scratch, and by the end of this article, we’ll create our first model trained on Vertex AI.
