#machine learning


How to use Machine Learning for IoT analysis

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.

Interview with ML Conference speakers Tina Nord & Kathleen Jaedtke

ML at its best: How the communication between human and machines works

Machine learning enables customized conversations between man and machine that can result in buying decisions. We asked Tina Nord and Kathleen Jaedtke to explain how this can be achieved through the use of dialogue-oriented technologies. Let’s take a look at how communication between man and machines works.

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.

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.

Crossing the divide from theory to reality

Studio.ML bridges the gap between data scientists & DevOps engineers

There are a number of issues that arise when data scientists and ML researchers meet DevOps to try to deploy, audit, and maintain state-of-the-art AI models in a production and commercial environment. Peter Zhokhov and Arshak Navruzyan discuss a new open source software tool called Studio.ML, which offers a number of solutions to this problem.

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.

Machine learning for you and me

Fitting models with AWS AI

Amazon has been using predictive models for decades. Now, they want to put machine learning in the hands of every developer with their AWS AI division. In this article, Cyrus Vahid, an AI specialist from AWS, explains some basic models for deep learning and goes over how Amazon has a service for every use case.