The open source machine learning frameworks just keep on coming. Oryx 2 is focused on real-time, large scale machine learning and uses the power of 3 tiers. Grab it by the horns and create custom applications.
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.
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.
Did you know you can count bees with AI? We take a look at some real world use cases for machine learning that you might have missed.
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.
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.
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.
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.
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 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.
Wherever you are, you can livestream the opening keynote for the Machine Learning Conference and join Xander Steenbrugge as he discusses “Cracking open the black box of neural networks”.
An in-memory computing platform with continuous learning capabilities enables a range of real-time decision making use cases. What might some of these cases be and how will they affect the future of machine learning and deep learning?
Looking to deploy and monitor large-scale deep learning applications for the enterprise? Polyaxon makes it easier to manage workloads for teams without losing control of your data.
Manifold may just have the solution for a problem that has been facing many ML teams. Let’s take a look at Torus: a new toolkit that promises to bring DevOps practices to machine learning. Open up the box and see what’s inside.