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
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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
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The Sigmoid Advantage:

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Recommended Read

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.

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