MLOps SolutionsOperationalize machine learning lifecycle faster and maximize the impact of advanced analytics
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Build, train and deploy ML models at scale
Extracting maximum ROI from machine learning models remains a major challenge for companies, as more than 50% of models fail to reach production owing to silos complicating ML model deployment. Sigmoid’s MLOps practice combines data science, data engineering, and dataops expertise to build effective AI strategies and deliver business value. Our expertise in open-source and cloud technologies enables you to build custom MLOps solutions and maximize ROI. We help data-driven companies to accelerate time to business value for AI projects by 30% by strengthening ML model lifecycle management and overcoming model drift challenges.
ML pipelines maintained
ML models in production
Uptime SLA of ML models
MLOps best practices to solve AI/ML production hurdles
Gartner states that on average, only 54% of AI projects make it from pilot to production. This is attributed to the impediments that technology and business leaders face in moving ML models to production. The eBook discusses MLOps best practices to overcome challenges of training, deploying and maintaining model accuracy at scale with a proven framework.Download eBook
Enhance ML model lifecycle management
Expedite model training and testing, enable model repository and provision scalable infrastructure.
Maximize AI initiatives, leverage open-source and cloud-based solutions, and deploy scalable MLOps frameworks.
Batch or real-time business insights for reports/dashboards and downstream systems.
Sigmoid’s MLOps framework
Customer success stories
90% improvement in the pricing and promotions ML model runtime for a top hygiene company
- Reduction in model run time from 8 days to just 14 hours
- 87% reduction in cost per run
- Easy and scalable migration of ML models across geographies
Our other offerings in Data Engineering
Automated data pipeline solutions to generate insights faster and make smarter business decisions.
Modernization, migration, and optimization of cloud performance with agility and reliability for optimal data usage.
Managed services to help you automate end-to-end enterprise data infrastructures for agility, high availability, better monitoring, and support.