Builds

Chunks

Our build automation technology employs an advanced approach to handling large datasets by breaking them into manageable chunks and distributing the processing workload across multiple processors. This parallel processing strategy optimizes system utilization, enabling seamless and efficient handling of vast amounts of data.

Dividing the data into smaller segments and processing them simultaneously, significantly reduces build times and accelerates the overall data transformation process. This method ensures that computational resources are used effectively, enabling rapid processing of large-scale datasets and speeding up the preparation of data for model training.

Worker Transparency

Worker Transparency technology enables direct monitoring of individual worker processes by connecting to each processing unit. When triggered, the feature opens a web terminal, establishes an SSH connection to the worker machine, and accesses the screen session where the process is active. This level of access allows real-time observation of activity, including warnings or messages that might otherwise go unnoticed in the primary interface.

This capability ensures early identification of issues, smooth data processing, and timely resolution of anomalies. Enhanced monitoring improves troubleshooting efforts and provides comprehensive insight into data workflows, fostering greater transparency and reliability.

Incremental Builds

Incremental Build technology ensures that all data builds are incremental by default, computing only newly added features during each build cycle. Previously computed features remain intact, resulting in significant time and resource savings. This approach streamlines feature engineering, enabling quick updates and testing of new features without the need to recompute the entire dataset.

By reducing the computational overhead of feature updates, the technology supports rapid experimentation and refinement, allowing for efficient exploration and optimization of data features. This agility is essential for dynamic machine learning workflows, where frequent adjustments and enhancements to feature sets are often required.