How to move the needle in enterprise machine learning: Take it to production level

James is editor in chief of TechForge Media, with a passion for how technologies influence business and several Mobile World Congress events under his belt. James has interviewed a variety of leading figures in his career, from former Mafia boss Michael Franzese, to Steve Wozniak, and Jean Michel Jarre. James can be found tweeting at @James_T_Bourne.

Artificial intelligence (AI) is going to be an important transformational tool in the enterprise – that much is perfectly clear. Yet so far initiatives have been in silos, or individual contributions. What is going to be needed to take the next step?

A new research report from Algorithmia has focused on the ways companies of all sizes are exploring machine learning – and found that larger organisations have the advantage for now so long as they make the most of their lead.

The study, titled The State of Enterprise Machine Learning, surveyed more than 500 decision makers at companies of various sizes, and found organisations were swiftly increasing their investments in machine learning. Four in five said their investment had grown by at least 25% in the past 12 months – a number which rises to 92% at companies with more than 10,000 employees.

Use cases are primarily customer-focused for now, whether that is through gaining insights and intelligence, improving the customer experience, as well as retention.

The move towards the largest companies holding the aces in machine learning has been noted elsewhere. Venture capital firm Work-Bench released a report in August which asserted the behemoths were hoovering up the best talent in the space. The products resulting from this are what Algorithmia is defining as the ‘AI layer.’ Tools such as BigHead, developed by Airbnb, and Cortex, from Twitter, are infrastructure to help manage ML portfolios and deployments across an organisation.

Yet there are various disparities which still need to be overcome. More than half (55%) of those polled said their company’s machine learning efforts were being driven by the engineering and technical side, while a further 37% said it was directed by management.

The biggest challenge, as cited by a majority of respondents, was productionising machine learning models. So what can be done about this? Cloudera hopes to have a solution.

The company is aiming to be at the heart of more concerted enterprise AI efforts through an ‘industrialisation’ process. Organisations will be able to build, deploy and scale ML and AI applications and make it repeatable, in what Cloudera is calling an ‘AI factory.’

Cloudera sees the gap in the market down to there being no platform available today which unifies and powers all AI and ML workloads – a lament similar to that from Algorithmia.

“The modern enterprise will be a network of intelligent applications powered by machine learning and AI,” said Hilary Mason, general manager for machine learning at Cloudera. “Right now, the effort that goes into building ML and AI solutions is heavily skewed towards allocating and maintaining underlying infrastructure, instead of building a compounding set of capabilities that can deliver increased business values.

“AI deployments should be boring, meaning the process for deploying and scaling these types of applications should be routine,” added Mason. “We should be paying attention to the value generated, not to the technology. This is why Cloudera is evolving its products and platform to industrialise the process of delivering AI solutions at enterprise scale.” Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London and Amsterdam to learn more. Co-located with the  IoT Tech Expo, Blockchain Expo and Cyber Security & Cloud Expo so you can explore the future of enterprise technology in one place.

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