We co-create innovative solutions that drive meaningful impact
Accelerate product development cycles with faster development and deployment of models
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Reduce operational costs by automating repetitive tasks and optimizing resource usage
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Improve model performance through continuous monitoring and tuning of models
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Meet regulatory compliance requirements through data governance
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Manage different versions of models and the data they were trained on
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Define and implement an application development lifecycle tailored for data science projects. Educate teams, and implement in scope management system (ex: Azure DevOps, or Jira + github).
Design and implement MLOps platform supporting your Data Science lifecycle. Specifically including experiment management, feature store, and cloud processing environment. Key considerations are performance, monitoring, and drift detection.
Implementation, production deployment and/or maintenance of new feature and metric stores or implement a 3rd party feature store such as Databricks into the MLOps platform.
Data science models are often developed with small data and encounter various problems when deployed into production to run on a big data. We have significant experience in making sure the models are tested in a distributed environment. We consider necessary factors to ensure that the models run successfully on real production environments.
Utilizing our BI expertise, we provide tailored, data-driven strategies that boost performance and growth, with comprehensive support from start to finish.
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Focused on data-driven strategies
that boost performance and growth
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