Common Python API for cloud-based AutoML services would allow data scientists to train their data sets against multiple AutoML models Credit: Thinkstock Automated machine learning tools vendor Auger.AI is developing a Python API and tools to multiple cloud-based AutoML services, allowing data scientists to train data sets against multiple AutoML models to yield the best-possible predictive model. Called A2ML, for Automate AutoML, the open source project consists of an API and command-line tools, which are currently in a beta stage. The plan calls for providing a common API for cloud-based AutoML services. The open source API works with “second generation” AutoML APIs including Auger.AI’s own API, Google Cloud AutoML, and Azure AutoML. With automated machine learning, or AutoML, 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 must be configured, 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 download a2ML from GitHub. Machine LearningPythonAnalyticsCloud Computing