#machine learning

We like to keep a finger on the pulse of the developer community. Today, we take a look at SlashData’s 2018 report on developers. Where are we going, what trends are we following, and what tools are we using? It looks like a lot of machine learning, serverless, and JavaScript.

Stop sharing machine learning models like it’s the stone age. Comet.ml intends to become the GitHub of ML, offering data scientists a simple, easy to use tool to share, compare, and optimize their machine learning models.

Apache Spark 2.3 was released earlier this year; it marked a major milestone for Structured Streaming but there are a lot of other interesting features that deserve your attention. We talked with Reynold Xin, co-founder and Chief Architect at Databricks about the Databricks Runtime and other enhancements introduced in Apache Spark 2.3.

Google’s machine learning framework TensorFlow is on the rise. We spoke to Christoph Henkelmann at ML Conference 2017 about its benefits in the enterprise and the reasons for using Java in this context. Furthermore, we talked about new trends in the world of machine learning.

TensorFlow 1.7 has just arrived. We take a look at one of the cool new features in the latest release: full integration for TensorRT! What does that mean for our favorite machine learning project? Faster performances, for one thing.

How can you design artificial intelligence to benefit society? How do you integrate smart systems into business life? Prof. Wolfgang Henseler addressed these questions in his keynote at the ML Conference 2017, “It’s all about machines and creating their minds”.

Want to learn machine learning? Or do you need to brush up on basic concepts? Today, we go over two essential reference materials for anyone just starting out on their machine learning adventure: the machine learning glossary and the rules of ML.

The internet’s favorite open source machine learning project is back with another update. What’s in TensorFlow 1.6? We take a look at some of the major features and improvements, bug fixes, breaking changes, and other issues.

AI is coming. Adoption of artificial intelligence grew globally in 2017 in a variety of industries. But where is it heading? In this article, Kim Palko and Matthew Farrellee explain where they think this emerging tech is going in 2018.

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.