Typical Machine Learning Project Workflow

Machine learning is a discipline that uses computer algorithms to extract useful knowledge from data.

There are many different types of machine learning algorithms, and each one works differently. In general however, machine learning algorithms begin with an initial hypothetical model, determine how well this model fits a set of data, and then work on improving the model iteratively. This training process continues until the algorithm can find no additional improvements, or until the user stops the process.

A typical machine learning project will include the following high-level steps that will transform a loose data hypothesis into a model that serves predictions.

  1. Explore and experiment with and display findings of data
  2. Deploy automated pipelines of analytics workloads
  3. Train and evaluate models
  4. Deploy models as REST APIs to serve predictions

With Cloudera Data Science Workbench, you can deploy the complete lifecycle of a machine learning project from research to deployment.