After ingesting your data into Treasure Data, you can build a predictive model using Treasure Data queries, and workflows.
The typical machine learning pipeline for supervised learning is:
data preparation
building a model
evaluating the model
predicting unseen data with trained model
You can use Treasure Data Workflows to manage your supervised learning process. Treasure Data (TD) provides AutoML as a feature which can be configured within the familiar Treasure Workflow environment. Learn more about AutoML
By using Digdag Treasure Data operators within your TD Workflow, you can automate your machine learning from data preparation to prediction. Digdag Treasure Data operators include:
Digdag can run tasks in parallel, so you can simultaneously run independent tasks such as parameter tuning. Treasure Data Workflows enable you to make prediction tasks a periodic part of your product offerings. Having a stable way to run and evolve your machine learning processes in batches on an hourly or daily basis is a good way to evolve them and derive a better predictive model.
