August 2021 Summaries

The journey to real-time machine learning adoption is complex, requiring deep changes within an organization. It typically starts with traditional analytics, followed by analytical ML, then operational ML, and finally real-time ML, each step building on the previous one as organizations mature in their use of machine learning. Real-time ML requires rapid deployment of models at scale, low-latency prediction, and the ability to consume real-time data sources, making it an aspirational objective for most enterprises. To achieve this, organizations need to develop data science expertise, bring in ML engineering expertise and processes, adopt MLOps tooling, and operationalize complex data pipelines with sub-second freshness.

Tecton's low-latency streaming pipelines automate the process of transforming data from streaming sources into fresh features for real-time machine learning models, eliminating the need to build custom pipelines and reducing deployment timelines by weeks or months. These pipelines provide sub-second feature freshness, ensure enterprise-grade uptime, latency, and throughput, combine batch and streaming data to backfill features, and efficiently process time window aggregations at scale. By contrast, conventional custom streaming pipelines often require significant lead times, ongoing pipeline management, and plumbing, resulting in higher costs and reduced prediction accuracy due to training / serving skew.