Remove Data Integration Remove Experimentation Remove IoT Remove Machine Learning
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The DataOps Vendor Landscape, 2021

DataKitchen

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Testing 300
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What Stands Between IT and Business Success? Data Complexity

CIO Business Intelligence

IT teams grapple with an ever-increasing volume, velocity, and variety of data, which pours in from sources like apps and IoT devices. At the same time, business teams can’t access, understand, trust, and work with the data that matters most to them. Innovation at integration points.

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P&G turns to AI to create digital manufacturing of the future

CIO Business Intelligence

The company has already undertaken pilot projects in Egypt, India, Japan, and the US that use Azure IoT Hub and IoT Edge to help manufacturing technicians analyze insights to create improvements in the production of baby care and paper products. It also involves large amounts of data and near real-time processing.

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Customer Experience and Emerging Technologies: My CXChat Summary on Artificial Intelligence, Machine Learning and the Customer

Business Over Broadway

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. IoT Artificial Intelligence. Start with understanding your problem first and what you want to solve.