Which stage of the machine learning lifecycle involves gathering raw sensor data from IoT devices into cloud storage?

The stage of the machine learning lifecycle that involves gathering raw sensor data from IoT devices into cloud storage is data ingestion. This process is crucial because it serves as the initial step in preparing data for analysis and model building. During data ingestion, data from various sources, such as IoT devices, is collected and transferred into a central repository or cloud storage where it can be accessed and utilized for further processing.

During this stage, data can be ingested in real-time or in batches, depending on the requirements of the application and the nature of the data being collected. This allows for the accumulation of large volumes of raw data, which can include sensor readings, environmental conditions, and other relevant information.

Once the data is ingested, it can then proceed to other stages of the lifecycle, such as data cleaning, where it is organized and prepped, and data transformation, where it may be formatted or manipulated to suit model training. However, data ingestion is specifically focused on the initial gathering and storage of data, making it the correct choice in this context.