Good data governance in the platform age

COMMENTARY

As more businesses turn to cloud platforms on which to build their content applications, especially as platform ecosystems evolve to encompass the best machine learning and artificial services available, what should they look for to make sure they stay on the right side of their own data governance policies and comply with regulatory mandates, such […]

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Google Cloud AutoML: no longer just bots-for-boffins

COMMENTARY

This week saw Google announce early alpha access to its Cloud AutoML service, designed to bring custom machine learning models to the general developer population (i.e. those without any specific deep learning expertise – not just deep learning researchers, or the data scientist community). By Google’s reckoning, that opens up the power of deep learning […]

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It’s ML-with-everything, as AWS equips builders for (AI) business

COMMENTARY

As much as a conference as wide-ranging as AWS re:Invent can have a focus, last week’s main message was that developers (“builders”, in CEO Andy Jassy’s parlance) need to embrace machine learning techniques in order to deliver the next generation of applications everywhere. It’s AWS’ mission to make that as accessible as possible. It’s fair […]

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Salesforce Einstein, Thunder and Lightning – just hooks to grab your attention?

COMMENTARY

Salesforce’s World Tour rolled into London last week. The company focused heavily on how its Intelligent Customer Success Platform enables developers to pull together IoT (Thunder IoT Cloud) and AI (Einstein) capabilities to power sales and marketing apps (built on the Lightning Platform). When Salesforce talks ‘IoT’, it’s really talking about real-time event processing. It’s […]

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