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#machine learning

Training AI services in the cloud

AI as smart services for everyone

If you cannot or do not want to build an AI project from scratch, you have countless choices of ready-made services. But what can you do if the finished services do not fit the project? Customizable AI and ML models in the cloud, which you can train with your own data, provide a remedy.

Machine learning at its finest

Facebook’s Getafix is a clever tool that learns how to fix bugs automatically

Facebook is on a roll! The company recently announced that they would soon release some internal tools and they did not disappoint. The latest tool to be open sourced is Getafix, which learns from engineers’ past code fixes to recommend bug fixes. Getafix aims to let computers take care of the routine work under the watchful eye of a human. Let’s take a closer look.

See Philipp Beer speak at the ML Conference on 5 December 2018 in Berlin!

“Designing proper data collection today improves the quality of ML outcomes tomorrow”

Machine learning may have all sorts of use cases, but forecasting? In honor of the upcoming ML Conference, we talked to Philipp Beer about how data scientists can utilize ML in statistical forecasting. We talk about the advantages and disadvantages of modern vs. classical methods, how can one decide between the two, and where should they turn when they need good predictions for their business KPIs.

Makes machine learning on Kubernetes easy, portable, and scalable

Kubeflow 0.3 brings better multi-framework support

It’s been three months since Kubeflow 0.2 was released so now it’s time for 0.3 to shine. This release provides easier deployment and customization of components and better multi-framework support. In this article, we’ll have a look at some of the highlights.

The reality of modern technology

AI and machine learning in software development: Benefits for developers

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.

Watch Sigrid Keydana's talk at ML Conference 2017

Coding deep learning: The absolute minimum an interested developer should know

Deep Learning is all the hype these days, beating another record most every week but writing code for deep learning is not just coding – it really helps if you have a basic understanding of what’s going on beneath. In this session from last year’s ML Conference, Sigrid Keydana offers a quick lesson on deep learning, as well as some tips and tricks for developers who’d like to dip their toes into this topic.

Benefits

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.