Remove Experimentation Remove IoT Remove Optimization Remove Testing
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How to Launch Your AI Projects from Pilot to Production – and Ensure Success

CIO Business Intelligence

A developing playbook of best practices for data science teams covers the development process and technologies for building and testing machine learning models. Have business leaders defined realistic success criteria and areas of low-risk experimentation? Are data science teams set up for success?

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How Svevia connects roads, risk, and refuse through the cloud

CIO Business Intelligence

But today, Svevia is driving cross-sector digitization projects where new technology for increased safety for road workers and users is tested. Taking out the trash Division Drift has been key to disruptively digitize Svevia’s remit with the help of the internet of things (IoT), data collection, and data analysis.

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What Heineken’s CIO is brewing for better connectivity

CIO Business Intelligence

The firm’s connected brewery IoT platform, for instance, is being used for data ingestion and edge computing in breweries, enabling local teams to analyze, adjust, test and optimize production processes, with this in-turn allowing operations to leverage real-time and historical data to support the workers on the shop floor.

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The DataOps Vendor Landscape, 2021

DataKitchen

Testing and Data Observability. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Testing and Data Observability. Production Monitoring and Development Testing.

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