Success Case | Global AI and ML-Ops Collaboration Platform
Multinational pharmaceutical company that has a annul turnover of 41.000 MM euros.
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The Challenge
In R&D labs and production systems across multiple product lines, the Customer was gathering petabytes upon petabytes of real time data which are analyzed by local operators of machines and systems. Data analytics collaboration is hindered by similar but different millions of tags. Best practices could not be identified, and too often similar products and production processes were re-invented. Consequently, models for Machine Learning were incompatible and not reusable.
The Solution
Keepler analyzed technical model serving technologies from cloud and open-source providers, recommending and implementing an MLOps pipelines for multi-account setups and a centralized deployment workflow. The model serving platform is open for product groups in various divisions of the Company to develop and make their AI models in available across the enterprise.
The Result
Keepler is a full-stack analytics services company specialized in the design, construction, deployment and operation of advanced public cloud analytics custom-made solutions. We bring to the market the Data Product concept, which is a fully automated, public cloud services-based, tailored software that adds advanced analytics, data engineering, massive data processing, and monitoring features. In addition, we help our customers transition to using public cloud services securely and improve data governance to make the organization more data-centric.
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