Success in machine learning is 90% about data

Many companies struggle to take control of their data due to the pace of change in the data landscape -

as data keeps growing, new tools keep emerging, but engineering best practices are not yet fully formed.

Building a data system that serves machine learning applications adds additional complexities and considerations, such as:

• Supporting feature time-travel - to avoid data leaks into models
• Enabling data scientists to experiment with new features at scale - and version their work
• Feature engineering pipelines and feature stores - for structured and semi-structured data
• Pre-processing pipelines and indexing - for unstructured data such as images, video, or voice
• Monitoring and maintaining data quality - always a challenge, but even more so for ML models