ML Conference
The Conference for Machine Learning Innovation


Scalable Programming

Java continuously introduces new, useful features. For instance, Java 8 introduced the Stream API, one of the biggest highlights of the past few years. But is aggregating data with the Stream API a panacea? In this article, I’d like to explore if there’s a better alternative for certain cases from a complexity perspective.

Take Control of ML Projects

The decision to move Elasticsearch to proprietary licensing awakened a sleeping giant. The open source community rapidly flexed its muscle to ensure a true open source option for fast and scalable search and analytics—which many users depend on for ML projects—would continue to be available. The result is OpenSearch, a community-driven hard fork of Elasticsearch 7.10.2, built with Apache Lucene and available under the fully open source Apache 2.0 license.

Why are we doing this anyway?

Modularization is frequently discussed, but after some time, the speakers realize that they don’t mean the same thing. Over the last fifty years, computer science has given us a number of good explanations about what modularization is all about—but is that really enough to come to the same conclusions and arguments?

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.

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!.

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!
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Behind the Tracks