A few decades back the job role called data scientists didn’t exist. There were employees who used to analyze, organize, and clean the data. But they were no actual profession like data scientists. Modern-day data scientists possess exceptional skills which many firms are looking for.

If one wants to be a part of this growing career and is interested…

As a business, your ability to retain the market’s competitive edge requires you to balance the necessity and difficulty for new product development. It is where the new product development process comes into play. It helps you test the new product’s sustainability for the market needs and its economic usefulness.  

Here are some of…

In the post-AI and the future of autonomous systems, we saw that AI driven autonomous systems may take off independently of autonomous cars.

Roboat from Amsterdam is one example of the shape of things to come.

In essence, as the name…

Engineering product development is undergoing rapid change. Products are becoming complex while development & launch timelines are getting crunched, with expectations of reduced product and product development cost. Oil & Gas (O&G) has adopted technology and has implemented best practices to address these challenges, without lowering the…

Healthcare is one of the most data-intensive industries in the world. With hospitals, clinics, and doctors all collecting patient information daily, it's no wonder that healthcare analytics is growing at an exponential rate.…

Enterprise machine learning deployments are limited by two consequences of outdated data management practices widely used today. The first is the protracted time-to-insight that stems from antiquated data replication approaches. The second is the lack of unified, contextualized data that spans the organization horizontally.

Excessive data replication…

The creation of a DigitalTwin knowledge graph data model confronts the need for access to measurement data in order that the DigitalTwin can create timely performance metrics, identify promptly performance issues, and so on.

However, the quantity of raw data in an Industrial…

Deep Learning (DL) techniques have changed the field of computer vision significantly during the last decade, providing state-of-the-art solutions for classical tasks (e.g., object detection and image…

In the lingo of artificial intelligence, subjecting the machine learning models to real-world environments wherein they can act on the real-world data and generate the prognostications for the real-world problems is called…