AI and science stand to complement each other very well, with the former seeking patterns in data and the latter dedicated to discovering fundamental principles that give rise to those patterns.

Here are eight common challenges data scientists face, including understanding business goals, communication with non-technical colleagues, and ongoing data maintenance.

Software Engineering Challenges In The Age Of AI: ... (AI engineers, ML experts and data scientists). Second, AI has applications across various industries, not just technology.

The demand on businesses to act instantaneously with the data has never been greater in the current digital first economy.

Data practitioners come across many challenges throughout the data management lifecycle. Let us learn about the most common day-to-day challenges we face and how to overcome them. By the time you ...

These student researchers tackled a key challenge in polar research by creating a communication system that could transform ...

The bigger problem, though, is that the datasets are very large. And even in a local environment, where the customer has 5,000 or 10,000 servers in their own data center and they need to access the ...

In this week’s Computer Weekly, our latest buyer’s guide examines best practice in data engineering and the importance of data skills. Labour announced its first digital government strategy ...

Whilst the number of citizen data engineers will continue to rise, this will not reduce the demand for qualified data engineers. Colleges and businesses will need to continue developing ...

Tech & Science Data engineering for scalable AI in cloud environments: Reddy Srikanth Madhuranthakam’s contributions. Srikanth’s work has been instrumental in optimizing data pipelines ...