Building Custom ML Models on Google Cloud
In the last chapter, we implemented AI/ML workloads by letting Google do all of the work for us. Now is the point at which we’re going to elevate our knowledge and skills to an expert level by building our own models from scratch on Google Cloud.
We will use popular software libraries that are commonly used in data science projects, and we will start implementing some of the concepts we discussed in previous chapters, such as unsupervised ML (UML) and supervised ML (SML), including clustering, regression, and classification.
This chapter covers the following topics:
- Background information – libraries
- UML with scikit-learn on Vertex AI
- Implementing a regression model with scikit-learn on Vertex AI
- Implementing a classification model with XGBoost on Vertex AI
