Feature (machine learning) In machine learning and pattern recognition, afeature is an individual measurable property of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression.
MACHINE LEARNING FEATURES
- Google services are designed to work together. It works with Cloud Dataflow for feature processing, Cloud Storage for data storage and Cloud Datalab for model creation.
- Build better performing models faster by automatically tuning your hyperparameters with HyperTune, instead of spending many hours to manually discover values that work for your model.
- Managed Service
- Focus on model development and prediction without worrying about the infrastructure. Managed service automates all resource provisioning and monitoring.
- Scalable Service
- Build models of any data size or type using managed distributed training infrastructure. Accelerate model development, by training across many number of nodes, or running multiple experiments in parallel.
- Notebook Developer Experience
- Create and analyze models using the familiar Jupyter notebook development experience, with integration to Cloud Datalab.
- Portable Models
- Use the open source TensorFlow SDK to train models locally on sample data sets and use the Google Cloud Platform for training at scale. In future phases, models trained using Cloud Machine Learning can be downloaded for local execution.