Jul
20,
2021
Top 5 reasons to attend ML Conference
<div style="text-align: justify;">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...
Jun
9,
2021
Anomaly Detection as a Service with Metrics Advisor
<div style="text-align: justify;">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...
Tools & Processes for MLOps
<div style="text-align: justify;">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...
Mar
15,
2021
On pythonic tracks
<div style="text-align: justify;">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...
Feb
16,
2021
Understanding Language is a Frontier for AI
<div style="text-align: justify;">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...
Jan
7,
2021
Real-time anomaly detection with Kafka and Isolation Forests
<div style="text-align: justify;">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...
Dec
16,
2020
Let’s visualize the coronavirus pandemic
<div style="text-align: justify;">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...
May
5,
2020
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...
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...
Feb
11,
2020
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...