Developing machine learning (ML) pipelines has never been easier than when using the Astro Cloud IDE. Data scientists frequently develop their ML pipelines using notebooks, which provide an interactive environment well-suited for iteratively developing the Python and SQL functions needed to clean data, prepare features, and train models.
The Astro Cloud IDE provides a notebook-inspired environment designed specifically for writing Python and SQL functions without any Airflow-specific boilerplate code. Data scientists can write the logic for their pipelines in a familiar environment, and the Astro Cloud IDE will automatically convert it to an Airflow DAG.
In this webinar, we show how the Astro Cloud IDE is the easiest way to develop and test your ML pipelines and schedule them with Airflow
