Keeping an Eye on AI
Your machine learning model is trained and finally running in production. But that was the easy part. Now, the real challenge is reliably running your machine learning system in production. For this, monitoring systems are essential. But while monitoring machine learning models, you must consider some challenges that go beyond traditional DevOps metrics.
Feb
10,
2022
Python Developers live in Visual Studio Code
With over 18 million monthly users, VS Code has become one of the most popular and fastest-growing text editors in the world. To learn more about why over 3.7 million of them find VS Code to be the perfect habitat for Python development and data science work, keep on reading!.
Nov
9,
2021
The Trends Shaping Natural Language Processing in 2021
According to research from last year, 2020 had an impact on business globally, but NLP was a bright spot for technology investments. That momentum has carried through to this year, but even still, we are just at the tip of the iceberg in terms of what NLP has to offer.
Oct
12,
2021
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.
Sep
14,
2021
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.
Jul
20,
2021
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!
Jun
9,
2021
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?
Mar
15,
2021
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.
Feb
16,
2021
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.