The Exadel Blog
OKRs (Objectives and Key Results) vs. KPIs (Key Performance Indicator): What’s the Difference?
How do Objectives and Key Results (OKRs) differ from Key Performance Indicators (KPIs)? Check out our explanations.
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How to explain OKRs (Objectives and Key Results) in plain English
What are OKR’s and how do they help IT and business leaders measure outcomes?
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Exadel Recognized for MarTech Leadership for Toolbox for Adobe Experience Manager
Exadel was recently recognized as a leader in MarTech Cube for our Toolbox for Adobe Experience Manager.
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QA best practices: How to Do Smart TV Application Testing Right
As interest in Smart TV apps grows, it is essential for QA teams to ensure their quality. But the QA process for TV apps is different from that of other formats. Here, the Exadel QA team shares Smart TV application testing tips.
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CompreFace Version 0.5 is Live! Here’s What’s New
Our team is working hard to improve our CompreFace solution and make it more user-friendly. Meet CompreFace version 0.5, which boasts lots of new features, improved scalability, and InsightFace and GPU support.
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A New ML1 Version for the Atlassian Data Center Goes Live. ML1 is Now Available to Data Center Users
We updated our Jira plug-in ML1 in response to recent Atlassian policy changes. See what changed and how our users will benefit from it.
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AEM Experience Fragments: URL Externalization with Adobe Target
In this article we will show you a way to deal with incorrect links which are sent to Adobe Target.
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How to Get Started with Continuous Monitoring
What is continuous monitoring and what are the best practices for doing it efficiently? CTO Oleg Boyko breaks down everything you need to know about continuous monitoring.
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Appery.io Recognized in G2 Spring 2021 Grid Reports
Appery.io has been recoginzed in G2’s Spring 2021 Grid Reports for low code and rapid application development. The Spring 2021 Grid Reports are based on G2’s unique algorithm, which calculates customer satisfaction and market presence scores in real-time.
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ML.NET: A Robust Framework for Implementing Machine Learning in .NET Environments
Python is a good fit for Machine Learning, but there can be a few difficulties if other parts of the system are written using C#. In this article, we take a closer look on ML.NET − a Machine Learning library for C# that helps solve these problems and delivers Machine Learning features in a .NET environment more quickly.
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