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.
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
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.
