Machine Learning Operationalization (MLOps)

End-to-end Life Cycle of ML Models

Maximizing Business Value of ML models

Gartner estimates that through 2023, 50% of IT leaders will struggle to move their AI projects past proof of concept (POC) to a production level of maturity. This is largely due to the complexities involved in the handoff between data scientists and data engineers, life cycle management of ML models and presence of multiple models in production.

Sigmoid’s MLOps enables the operationalization of the end-to-end pipeline that supports the continuous delivery and continuous integration of models in a production environment. We help enterprises deploy machine learning models into production quickly and effortlessly, thus facilitating faster return on your ML investment. Our dedicated team of data engineers work with data scientists from the start, to overcome prevalent challenges with coding practices, data quality and large discrepancies in training and testing data sets ensuring that models are smoothly deployed to production environments without any snags.

Six stages of ML 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
  • Data quality – fully vetted cleaning logic
  • Unit tested
  • Integration with GitHub- versioned deployments
  • Staging and Production environment
  • Logging and Monitoring
  • Alerting
  • Basic retry ability and operational readiness
  • Data Versioning
  • Triggers and dependencies defined
  • Workflow orchestrator integration
  • Tuned to meet SLA
  • Autoscaling

Sigmoid enables productionizing ML models at scale

A recent Sigmoid poll showed that 43% of organizations found it challenging to productionize and integrate ML models. It is here where a dedicated team of ML model experts providing continual support and enhancement can make a real difference.

Sigmoid’s MLOps practice provides the right mix of data science, data engineering and data ops expertise, required to build, test & maintain ML models that consistently deliver business gains amidst changing modelling needs. Our proficiency across a vast breadth of open source and cloud technologies and deep experience in scaling ML models for F500 companies managing Terabytes of data daily for analytics teams spread across the globe.


Empowering Data Science teams

Across enterprises we have seen that 80% of the data scientist’s time is spent in preparing and managing data for analysis. It is important to structurally align the data science and data engineering teams to ensure collaboration at various stages of the ML Model producitonization and manage machine learning operations. This includes identifying skills, defining roles and early collaboration points in the development cycle for smooth deployment of models in production environments.


Sigmoid follows a standardised process while taking any ML model into production. This includes – Setting up infrastructure for model monitoring, logging & alerting for maintenance and debugging, tracking model performance metrics & artefacts and obtaining acceptance from cloud security & network teams.

Why work with Sigmoid?


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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Taking ML models from PoC to production with MLOps

The e-book discusses how to effectively approach MLOps using tried and tested methods

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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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