ML Conference
The Conference for Machine Learning Innovation


Let’s visualize the coronavirus pandemic

Since February, we have been inundated in the media with diagrams and graphics on the spread of the coronavirus. The data comes from freely accessible sources and can be used by everyone. But how do you turn the source data into a data set that can be used to create something visual like a dashboard? With Python and modules like pandas, this is no magic trick.

Explainability – a promising next step in scientific machine learning

With the emergence of deep neural networks, the question has arisen how machine learning models can be not only accurate but also explainable. In this article, you will learn more about explainability and what elements it consists of, and why we need expert knowledge to interpret machine learning results to avoid making the right decisions for the wrong reasons.

Continuous Delivery for Machine Learning

In modern software development, we’ve grown to expect that new software features and enhancements will simply appear incrementally, on any given day. This applies to consumer applications such as mobile, web, and desktop apps, as well as modern enterprise software. We’re no longer tolerant of big, disruptive software deployments. ThoughtWorks has been a pioneer in Continuous Delivery (CD), a set of principles and practices that improve the throughput of delivering software to production in a safe and reliable way.

Tutorial: Explainable Machine Learning with Python and SHAP

Machine learning algorithms can cause the “black box” problem, which means we don’t always know exactly what they are predicting. This may lead to unwanted consequences. In the following tutorial, Natalie Beyer will show you how to use the SHAP (SHapley Additive exPlanations) package in Python to get closer to explainable machine learning results.

Deep Learning: Not only in Python

Although there are powerful and comprehensive machine learning solutions for the JVM with frameworks such as DL4J, it may be necessary to use TensorFlow in practice. This can, for example, be the case if a certain algorithm exists only in a TensorFlow implementation and the effort to port the algorithm into another framework is too high. Although you interact with TensorFlow via a Python API, the underlying engine is written in C++. Using the TensorFlow Java wrapper library, you can train and inference TensorFlow models from the JVM without having to rely on Python. Existing interfaces, data sources, and infrastructures can be integrated with TensorFlow without leaving the JVM.

Machine Learning finds its way into .NET with .NET Core 3

.NET Core is not only including WPF and WinForms in the new open source implementation of .NET - Microsoft now wants to make machine learning usable for everyone. That's why machine learning is now making its way into .NET Core with the ML.NET Framework. In this series of articles, we'll show you what ML.NET can do, what options the developer has available, what the tooling and APIs look like, and what’s happening behind the scenes.

Innovative machine learning with the Apache Kafka Ecosystem

Machine Learning (ML) allows applications to obtain hidden knowledge without the need to explicitly program what needs to be considered in the process of knowledge discovery. This way, unstructured data can be analyzed, image and speech recognition can be improved and well-informed decisions can be made. In this article we will in particular discuss new trends and innovations surrounding Apache Kafka and Machine Learning.

Behind the Tracks