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Your search for "machine learning" returned 437 results.

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Making improvements to user experience with ML

How machine learning is changing the travel industry

Machine learning’s growth continues as it permeates into unrelated industries. Travel booking might not seem like a good fit at first, but Wilco van Duinkerken of trivago explains how ML is innovating the way you find and book your next holiday.

Interview with Vish Abrams, Architect, Cloud Development at Oracle

GraphPipe makes deploying a machine learning model easy and comfortable

Oracle has just released a new open source tool: GraphPipe is designed to simplify and standardize the deployment of machine learning models. We talked to Vish Abrams, Architect, Cloud Development at Oracle about the new tool, its benefits, challenges and more.

How to avoid false starts and get ahead of everyone else

Does your company have a machine learning strategy?

Are you ready for machine learning? Do you have a plan? In this article, Atakan Cetinsoy from BigML goes over six things that every organization needs to be aware of when they’re devising their own machine learning strategy.

Not an easy skill

Applying machine learning to DevOps

How can developers learn to utilize machine learning in their DevOps practice? In this article, Prasanthi Korada goes over some basic approaches that can help developers apply cutting edge tech like machine learning to their everyday work.

Watch Sumanas Sarma and Rob Hinds' JAX London 2017 session

Agile machine learning: From theory to production

With artificial intelligence and machine learning becoming increasingly relevant for modern enterprises, many companies might be feeling the pressure to invest in an AI strategy, before fully understanding what they are aiming to achieve. In this session, Sumanas Sarma and Rob Hinds explain how you can go from theory to production in adopting machine learning solutions.

Minimizing machine learning risks

How to develop machine learning responsibly

Machine learning inevitably adds black boxes to automated systems and there is clearly an ethical debate about the acceptability of appropriating ML for a number of uses. The risks can be mitigated with five straightforward principles.

Looking to the future

The state of machine learning in 2018

The future of digital technology is here. 2017 saw incredible progression for things like data science, artificial intelligence, and machine learning. Where will they go in 2018? In this article, Maria Thomas explores the future of data science and how well it can be combined with predictive analysis.

Open source makes everything better

Why are so many machine learning tools open source?

Open source and machine learning go together like peanut butter and jelly. But why? In this article, Kayla Matthews explores why many of the best machine learning tools are open source.