Machine learning is complicated, but it’s becoming easier and easier to grasp with new tools and platforms. In preparation for AWS re:Invent 2018, AWS updated SageMaker. What are the newest features?
New ways of handling large amounts of data by building more layers of artificial intelligence into computer systems have been allowing developers and businesses to create computer systems that work for them. In this article, Paul Bates explains why the future of consumerism and business optimization relies on machine learning and what role developers play in all this.
Last week, we covered artificial intelligence in our tech history course. This week, we dive deep into the hottest trend in this field: machine learning. ML’s near-human performance in tasks like image recognition masks some really strange issues, because ML logic is not like our Earth logic.
We have some more machine learning news for you. Meet Kalimdor, a Typescript based machine learning library that promises to solve your ML problems as well as teach you how ML algorithms work.
Many of the most exciting high-tech projects nowadays include bringing together knowledge from two or more well-established and fast-growing fields. One of the prominent examples is applying machine learning in order to filter and analyze the huge amount of data we obtain from the Internet of Things (IoT). But first, let’s see why IoT needs help from artificial intelligence in order to reach its full potential.
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.