Machine Learning Operations (MLOps)

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Deploy Machine Learning Models Faster

More than 50% of ML models fail to move from proof of concept to production, which remains a major machine learning challenge faced by companies. Various teams often work in silos leading to complexities in creating, managing, and deploying machine learning models.

Sigmoid’s MLOps practice provides the right mix of data science, data engineering, and DataOps expertise, required to operationalize and scale machine learning to deliver business value, and build an effective AI strategy. Our expertise in open-source and cloud technologies enables you to build custom ML solutions and maximize ROI. We help data-driven companies to accelerate time to business value for AI projects by 30% by strengthening ML model life cycle management and overcoming the challenges of model drift.

Speed Up Data Science Projects

Sigmoid enables businesses to efficiently build and manage machine learning projects throughout the development lifecycle. Our MLOps practice enables data-driven enterprises to consistently build, deploy and monitor ML models at scale.

Model Deployment

Unlock the full potential of AI investments
Leverage open-source and cloud-based solutions
Deploy repeatable Machine learning Operations framework

Model Performance Monitoring

Eliminate the last-mile hurdle of ML operational chaos
Fast-track the time to market of ML models
Monitor production and performance to take
proactive measures

Model Accuracy and Automation

Detect Model drift
Build ML pipelines
Ensure model accuracy and data consistency
Focus on innovation over operational issues

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End-to-End ML Model Lifecycle Management

Build and manage ML models
Improve ML model lifecycle management
Integrate MLOps end-to-end
Orchestrate and govern ML models through the
entire lifecycle

Contact us for an assessment of your end to end machine learning lifecycle

Overcome Machine Learning Challenges With Our MLOPs Framework

From identifying the business requirement to training ML models and maintaining them, MLOps services by Sigmoid brings together the best talent in Data Engineering, DataOps, and Machine Learning across the entire lifecycle to drive analytics success.

Setting up the right data lake and enabling scalable computing resources from the start
Choosing a Tech stack that ensures interoperability in modeling techniques and model Re-trainability
Adopting the right coding practices and following the right integration approach
Monitoring the model performance, carrying out periodic health checks, and automating model updates to eliminate model drift

Case Studies

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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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Automation of ML data pipelines using Spark

Sigmoid productionized the MAB model for a major restaurant chain that delivered tailored emails to over 12Mn customers resulting in 8% sales uplift

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E-Book: MLOps Best Practices to Solve AI/ML Production Hurdles

Taking ML models from PoC to production with MLOps

Gartner predicts that through 2022, only 20% of analytic insights will deliver business outcomes. This is attributed to the impediments that technology and business leaders face in moving ML models to production. The eBook discusses how to effectively approach MLOps using tried and tested methods.

Looking to productionize your ML models?