, and .
With , or , frameworks and services eliminate the need for data scientists to develop machine learning and deep learning models manually, and even reduce or eliminate the skills necessary to create them.
Auger.AI said that the cloud AutoML vendors all have their own API to manage data sets and create predictive models. Although the cloud AutoML APIs are similar—involving common stages including importing data, training models, and reviewing performance—they are not identical. A2ML provides Python classes to implement this pipeline for various cloud AutoML providers and a CLI to invoke stages of the pipeline.
The A2ML CLI provides a convenient way to start a new A2ML project, the company said. However, prior to using the Python API or the CLI for pipeline steps, projects , which involves storing general and vendor-specific options in YAML files. After a new A2ML application is created, the application configuration for all providers is stored in a single YAML file.
Where to download a2ML
You can from GitHub.
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