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
The Sigmoid Advantage:
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5 challenges to be prepared for while scaling machine learning models
Machine Learning (ML) models are designed for defined business goals. ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets.