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.