In the era of AutoML (Automated Machine Learning) manually defining a model, training it & deploying it may seem an outdated approach. However, having full control of the model from its definition to its deployment on the cloud allows a fine-grained, production-oriented, and less expensive approach.

Instead of letting the AutoML engine decide everything about the model and the hardware to use, we can do it by ourselves. This approach allows the experienced machine learning practitioner to have full control of the pipeline from the model definition to the hardware used for training and deploying.

In this talk, we'll see how to use TensorFlow Decision Forest (TF-DF) to define a simple regression tree, create a custom Docker image for its training, and train it using Vertex AI, the Google Cloud Machine Learning platform.
The trained model will then be deployed on Google Cloud and it will be ready to accept prediction requests. Eventually, the definition and usage of a client is shown.

Instead of doing all the steps using the web interface, we'll do almost everything using the not-so-known Go client for Vertex AI.