ML Conference
The Conference for Machine Learning Innovation


What is Data Annotation and how is it used in Machine Learning?

What is data annotation? And how is data annotation applied in ML? In this article, we are delving deep to answer these key questions. Data annotation is valuable to ML and has contributed immensely to some of the cutting-edge technologies we enjoy today. Data annotators, or the invisible workers in the ML workforce, are needed more now than ever before.

Neuroph and DL4J

In this article, we would like to show how neural networks, specifically the multilayer perceptron of two Java frameworks, can be used to detect blood cells in images.

Top 5 reasons to attend ML Conference

So you’ve decided to attend ML Conference but you don’t know how to break it to your boss that it is a win-win situation? Don’t worry, we’ve got you covered. Follow 4 simple steps and use these 5 arguments to show why your organization needs to invest in ML Conference!

Anomaly Detection as a Service with Metrics Advisor

We humans are usually good at spotting anomalies: often a quick glance at monitoring charts is enough to spot (or, in the best case, predict) a performance problem. A curve rises unnaturally fast, a value falls below a desired minimum or there are fluctuations that cannot be explained rationally. Some of this would be technically detectable by a simple automated if, but it's more fun with Azure Cognitive Services' new Metrics Advisor.

Tools & Processes for MLOps

Training a machine learning model is getting easier. But building and training the model is also the easy part. The real challenge is getting a machine learning system into production and running it reliably. In the field of software development, we have gained a significant insight in this regard: DevOps is no longer just nice to have, but absolutely necessary. So why not use DevOps tools and processes for machine learning projects as well?

On pythonic tracks

Python has established itself as a quasi-standard in the field of machine learning over the last few years, in part due to the broad availability of libraries. It is logical that Oracle did not really like to watch this trend — after all, Java has to be widely used if it wants to earn serious money with its product. Some time ago, Oracle placed its own library Tribuo under an open source license.

Understanding Language is a Frontier for AI

In recent years we have seen a lot of breakthroughs in AI. We now have deep learning algorithms beating the best of the best in games like chess and go. In computer vision these algorithms now recognise faces with the same accuracy as humans. Except they don’t, they can do it for millions of faces while humans struggle to recognize more than a few hundred people.

Real-time anomaly detection with Kafka and Isolation Forests

Anomalies - or outliers - are ubiquitous in data. Be it due to measurement errors of sensors, unexpected events in the environment or faulty behaviour of a machine. In many cases, it makes sense to detect such anomalies in real time in order to be able to react immediately. The data streaming platform Apache Kafka and the Python library scikit-learn provide us with the necessary tools for this.
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Behind the Tracks