Productionize ML Models
Productionize ML Models
The paramount step to transform ML models and realize business expectations
Creation of today’s cutting edge ML models and state-of-the-art R&D solve only a part of the business problem. Enterprises need to implement ML algorithms for vast amounts of data in order to realize the full potential of real-time data analytics. Sigmoid’s experienced Data Engineering team works across the system lifecycle in developing production scale platforms that automate – building, training and deploying of ML models.
Stages of Model Maturity
Enabling enterprises transition across the different stages of engineering maturity to a full-fledged automated system with automated training, testing, logging and alerting mechanisms
| Built on synthetic data with Target schemas in mind | Synthetic columns, schema |
| Built on actual data with a scaled down version of the data | Actual columns, data cleaning, transformation logic and data validation rules specified |
| Full scale implementation actual data | Fully vetted cleaning logic (data stats validation etc.) | Unit tested |
| Triggers and dependencies defined | Basic retry ability and operational readiness is done | Data versioning is defined | Some level of tuning to meet SLA |
| Integration with GitHub- versioned deployments | At least staging and prod environment |
| Logging and Monitoring | Alerting | Autoscaling |
Empowering Data Science teams
Empowering Data Science teams
Success Stories
ML Model Productionization to achieve 1:1 Marketing Effectiveness
Developed an architecture to productionize the MAB model by automating pipelines in AWS that updated a CRM platform to trigger personalized emails to end customers
Productionizing the Digital Demand Forecasting ML Models
Created a scalable and production ready end-to-end system that covered new products, categories and countries for a leading CPG Cosmetics firm
The Sigmoid Advantage:
