ASP.NET offers many benefits, such as improved security, easy updating, language independence and less overall code. With that said, .NET is not without errors and issues, even when working with a professional, such as this .NET development company. One common error is an Application_error in the Global.asax file. Let’s understand how to handle ASP.NET App’s Global.asax and other common errors in .NET.

Senior executive Tom Chmielewski joins Testlio in London January 18, 2021. London, United Kingdom. Testlio, the originator of networked testing, today announced an important new hire. Tom Chmielewski is now Executive Director, Strategic Development. He will lead Testlio’s sales expansion in Europe while also forging new global alliances.

Data science has become an important capability for enterprises looking to solve complex, real-world problems, and generate operational models that deliver business value across all domains. More and more businesses are investing in ML capabilities, putting together data science teams to develop innovative, predictive models that provide the enterprise with a competitive edge — be it providing better customer service or optimizing logistics and maintenance of systems or machinery.

One particular feature requested by our customers is 3D body mapping, and whether exporting 3D models and utilizing its visualization and filtering can be applied easily. This technical walkthrough shows you how to leverage Yellowfin to integrate 3D models within Yellowfin and then use them to create a fully interactive display in your dashboard.

Data is produced and consumed at volumes and speeds which were unimaginable just a decade ago.Top players have taken advantage of this growth. Tapping into data resources for actionable insights - aptly called the new oil - makes data-driven companies dominate their competition. But the proliferation of data can lead to growing pains. Companies find themselves increasingly incapacitated by the vast and messy nature of their in-house data.

Building machine learning (ML) and deep learning (DL) models obviously require plenty of data as a training-set and a test-set on which the model is tested against and evaluated. Best practices related to the setup of train-sets and test-sets have evolved in academic circles, however, within the context of applied data science, organizations need to take into consideration a very different set of requirements and goals. Ultimately, any model that a company builds aims to address a business problem.

Most blogs in my history are very focused on Industry 4.0’s digital transformation of the manufacturing industry, which in itself is pretty remarkable. By 2025, Industry 4.0 is expected to generate greater than $11 trillion in economic value as connected manufacturing processes, operations and their supply chains become more streamlined, efficient, agile and realize improved productivity, improved uptime and product quality.