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before vectice

Ravi faced significant delays in model development due to fragmented documentation across all data science tools and ad-hoc collaboration through emails and meetings with his peers.

after vectice

Accelerated model development and collaboration

Slashed cycle time by 50%

With Vectice, Ravi leveraged automated documentation to unify all the project information in one place and used the Vectice platform to streamline collaboration and avoid any last-minute issues.

Model Development Cycle: 4-6 months → 2-3 months
Documentation and communication: 30% of project time -> 10% of project time
Major Issues creating delays: 4-5 by project → 0-1 by project

before vectice

Joan struggled with reproducing modeling team results, inconsistent model documentation standards and manual documentation of validation testing.

after vectice

Enhanced model validation efficiency

Cut validation time by 75%

With Vectice, Joan can easily reproduce modeling results because everything is traceable and documented, including code, data lineage, and modeling details. Improved documentation reduces findings and back-and-forth with the modeling team, saving time. She also generates most sections of the final validation documents by passing model validation test results to Vectice macros and templates.

Validation time per ML model: 6-8 weeks -> 1-2 weeks
Number of model challenges: 15-20 per model → 5-7 per model
Consistency of model validation documents with internal validation standards: 50% -> 80%

before vectice

Marie struggled to keep track of projects, review documentation, and ensure her team followed best practices. Updates were scattered across various channels and technical tools, while day-to-day tasks were tracked in JIRA without proper project context and documentation.

after vectice

Improved project documentation and oversight

Approvals 50% faster

Vectice provided comprehensive project dashboards and templates with embedded best practices checks. It centralized documentation in one place, offering end-to-end project context. This made it easy to track project progress, provide feedback, verify adherence to best practices, engage with stakeholders, and maintain full project visibility.

Project oversight and documentation: 30% projects → 90% projects
Approval speed: Slow, 4-5 round of reviews → Fast, 1-2 round of reviews
Adherence to best practices: Poor < 30% → High > 70%

before vectice

Aihan lacked clarity on how models were built, making it difficult to automate a production pipeline and ensure the model behaved according to the original requirements. After deployment, there was no formal model business review, leading to overcommunication fatigue on non-actionable alerts and causing the team to eventually ignore the alerts.

after vectice

Streamlined model deployment and ongoing business review

Slashed cycle time by 50%

With Vectice, Aihan had clear documentation of how models were built, making it easier to automate the production pipeline and ensure consistent model behavior according to original requirements. Post-deployment, Vectice provided tools for formal business reviews, ensuring that models are delivering on their business metrics and allowing to calibrate monitoring alerts only on critical performance issues.

Deployment time: 6-8 weeks per model → 2-3 weeks per model
Alert Actionable Ratio (AAR): 20% -> 80%
Business Review: 20% completed → 80% completed