The fleet industry has faced challenges related to operational inefficacies, theft, fleet maintenance since time immemorial. Today AI is helping to solve these and other persistent problems of the industry. Is it possible to eliminate these challenges completely? Perhaps not, but with AI-powered solutions, it is possible to face these with greater efficiency.

When trying to gain business value through machine learning, access to best hardware that supports all the complex functions is of utmost importance. With a variety of CPUs, GPUs, TPUs, and ASICs, choosing the right hardware may get a little confusing. This blog discusses hardware consideration when building an infrastructure for machine learning projects.

What influences the customer buying decision for any product that they do not actually need?

Within the production pipeline, we want our machine learning applications to perform well on unseen data. It doesn’t really matter how well an ML application performs on training data if it cannot deliver accurate results on test data. To achieve this purpose, we use regularization techniques to moderate learning so that a model can learn instead of memorizing training data.

Deep learning, powered by deep neural networks, can deliver significant benefits to organizations on their transformation journey. Trends related to transfer learning, vocal user interface, ONNX architecture, machine comprehension and edge intelligence will make deep learning more attractive to businesses in the near future. There is no doubt that we will continue to see a growth in the application of deep learning methods in 2019 and beyond.

“Out of 1000 business decision makers, 98% agree that digital, including the delivery of digital

As edge devices gain greater computing power and machine learning becomes more mature, it becomes possible to infuse intelligence into edge devices. This article explores the impact that AI and ML will have on edge computing.

Enterprises nowadays are increasingly utilizing machine learning for acquiring, storing, and analyzing data in order

In the modern business landscape, most enterprises depend on machine learning (ML) applications to understand potential sources of revenue, recognize market trends, anticipate customer behavior, forecast pricing fluctuations, and ultimately, make informed business decisions. The development of these ML applications necessitates meticulous planning and a structured approach. The major steps in this process include defining the problem, cleaning, and preparing the data, engaging in feature engineering, conducting model training, and continually refining the model’s accuracy.