December 2024 Summaries

Training an accurate model is one thing, but ensuring it stays accurate in production is a whole other challenge. 91% of ML models degrade over time once they're deployed, with suboptimal feature engineering practices often being the culprit. This degradation occurs in two stages: pre-production and post-production. In pre-production, issues such as misinterpreted feature logic, inaccurate historical data, and incomplete training datasets can lead to poor model performance. Post-production, models face "model drift" due to changes in the world, including concept drift and data drift, where the relationship between inputs and outputs changes over time. To improve accuracy and prevent model demise, it's essential to get data scientists and engineers on the same page, combine different feature sources, generate accurate training datasets quickly, and rapidly iterate on features based on real-world feedback.

At Tecton, we've observed a consistent pattern of companies investing heavily in AI initiatives but struggling to operationalize them due to the complexity of productionizing data for AI. The issue lies not with model accuracy or technical talent, but with the vast difference between experimental and production data infrastructure. Data pipelines need complete re-engineering, data freshness requirements demand new infrastructure, scale requirements force architectural changes, training-serving consistency becomes a major challenge, and engineering resources get overwhelmed. This cycle perpetuates as better models require more data engineering iteration, leading to delays, reduced stakeholder confidence, and missed opportunities. A better path forward is provided by Tecton's platform, which bridges the data science-production gap by eliminating handoffs, providing consistent data across environments, building complex pipelines, automating scaling and optimization, and reducing engineering overhead.