finance_ops · workflow
Tide achieves 2x faster ML model deployment with Tecton feature platform, unlocking 7x more features and 2x more models
Tide's in-house feature store created bottlenecks that made it difficult to add features, caused unpredictable production changes, and produced slow iteration times with heavy engineering requirements, resulting in model deployments that took two to four months.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Feature definition
Users define and test features from diverse data sources using Tecton's declarative framework.
Tools used
TectonPython
Outcome
With Tecton, Tide cut model deployment time in half, deployed 2x more models, and achieved 7x more features with increased model accuracy, significantly accelerating time-to-value for new features.
What failed first
Tide's in-house feature store was unable to support the pace of real-time ML model development, producing unpredictable production changes and requiring heavy engineering resources.
Results
Time savedcut model deployment time in half
Volume2x faster
Grounding & classification
Source type: vendor customer story
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fraud detectionpredictive analyticsfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedfinancial servicesaccuracy improvementcycle time reductionthroughput increasevendor customer storyfinance opskyc amldata sync enrichment