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NEWS

AWS introduces HealthLake

Amazon HealthLake enables healthcare organisations to store, transform, and query health data in the cloud using machine learning for a complete picture of patient and population health.

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NEWS

GitLab acquires UnReview

Acquisition takes first step in building GitLab’s Applied Machine Learning for DevOps .

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Dataiku available in the Microsoft Azure Marketplace

Microsoft Azure customers worldwide now gain access to Dataiku, giving companies large and small an easy way to leverage the power of AI.

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ThoughtSpot and Databricks partner

ThoughtSpot launches support for Databricks allowing users to run search and AI-driven analytics directly on the Databricks Lakehouse Platform, powered by Delta Lake.

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Putting AI and ML in expert hands

Democratisation of Machine Learning makes operational experts more flexible to improve and predict process and asset performance.

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Intelligent automation can unlock $850 million annual value for CSPs

CSPs across North America, Europe, Latin America, EMEA and APAC disclose plans for automation.

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Acquisition takes first step in building GitLab’s Applied Machine Learning for DevOps .
Auto industry plans to make 44% of its factories smart in next five years – but companies must also...
Defining open standards is essential for deploying and governing machine learning models at scale fo...
Rolls-Royce Power Systems adopts Iotics’ operating environment and toolset to expand its digital eco...
GigaSpaces' InsightEdge has been selected by Magic Software Enterprises to power their Magic xpi end...
New report suggests organizations are missing a huge opportunity in automation by focusing on operat...
Latest Video

LinkedIn Automates All of the Easy Things, and Makes all of the Hard Things Easy

Hear LinkedIn’s senior SRE, Todd Palino, share how the company continually improves the state of its infrastructure, so that the developers who are rolling out applications have a framework that they can do it within, and they can do it safely. LinkedIn currently generates over 50 terabytes a day of unique metrics on applications. No human is going to look at 50 terabytes a day of data and get anything useful out of it, so LinkedIn relies on systems give them some useful signal out of all that noise. By moving down the road of machine learning, LinkedIn can now do anomaly detection using machine learning models.

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