The team from JetBrains has just released Datalore, a cloud-based web app for building machine learning models and creating rich visualizations in Python.
Machine learning is the next big thing in computing; are you ready for it? Hiring data scientists or ML experts isn’t easy or cheap. But the rise of machine learning-as-a-service (MLaaS) suggests that you won’t need to. Today, we take a look at five of the top machine learning service providers to see which one works the best for you.
The AI industry is never going to run out of the need for tech-savvy developers who can think out of the box. This technology is here to help us create better software which is safer than software created under traditional environments. In this article, Alycia Gordan explains why AI will teach developers a new mindset about the field they have been most passionate about.
Google is expanding its machine learning offerings with the all new Cloud AutoML. This service facilitates the use of machine learning models for developers and enterprises first starting out on their machine learning adventures. First up: image recognition!
Machine learning’s explosive growth has been fueled by a number of open source tools making it easier for developers to learn its techniques. We take a look at five of our favorite machine learning frameworks for Java and Python.
A team of researchers at Oak Ridge National Laboratory wrote a paper in which they argue that machines will write most of their own code by 2040. Does this mean that humans won’t be writing code at all? How will coding in 2040 look like? We talked with Jay Jay Billings, one of the authors about their ideas and the future of machine learning.
Machine learning can do all sorts of things: it can discover new exoplanets and apparently, it will help machines (in the not so distant future) write most of their own code by 2040.
Kubeflow brings composable, easier to use stacks with more control and portability for Kubernetes deployments for all ML, not just TensorFlow.
Artificial Intelligence and Machine Learning are all the rage right now. JAXenter editor Gabriela Motroc caught up with Sumanas Sarma and Rob Hinds at JAX London 2017 to talk about engineering best practices that can be applied to ML, their favorite programming languages and libraries for machine learning, and when it’s wise to jump on the ML bandwagon.
The most popular machine learning project becomes even more mobile-friendly with the introduction of TensorFlow Lite. Designed to be lightweight, cross-platform, and fast, this makes it even easier for machine learning models to be deployed on mobile or embedded devices.
What’s cooler than machine learning? Machine learning that’s made by machines. In Tile, a new machine learning language from Vertex.AI, crucial support structures are automatically generated to save time and effort.
It is always fascinating to see the versatile ways in which machine learning can be used. At Outfittery, algorithms help the experts select the most suitable outfits for customers — quite literally. In an interview at W-JAX 2017 in Munich, Jesper Richter-Reichhelm, CTO at Outfittery GmbH, explains how the company uses machine learning and which frameworks they use. He also tells us who makes better suggestions — human beings or machines.
TensorFlow 1.4 is here! The latest update to one of the most popular open source machine learning projects boasts big changes, new features, and even a couple of bug fixes.