Software Engineering Insights

With the continuous development in DevOps practice and its unparalleled benefits, now every organization wants to embed it in their software development lifecycle to make the deployment of software even faster and more efficient.

As software development gets complex, we see cross-functional teams collaborating to enable faster product delivery, optimize cost, manage infrastructure, and for several business benefits. An example of such a joint effort is ‘Cloud FinOps’ wherein finance, engineering, technology, and business teams aim at cloud financial management. 

Technology is embedded in every aspect of our lives. We rely on it for almost everything, especially to communicate with others. Simply, take the example of Siri or Alexa that we use in our day-to-day lives. Ever wondered how these virtual assistants understand and interpret our commands? How do they pick up terms and carry out the tasks according to our instructions? For example, when we ask Alexa to set an alarm for us.

Rolling out a new software system, infrastructure, or technology solution for an industry such as healthcare or personal finance has critical end-user outcomes. Technology Readiness Levels (TRL) refers to a robust scale often used by pioneering software organizations throughout their development efforts.

PyTorch and TensorFlow are two of the most widely relied-upon frameworks for Deep Learning (DL) research and industry implementation. While both these DL development frameworks haven't been around too long, they have made a significant mark in AI/ML research and innovation.  However, when it comes to which between PyTorch and TensorFlow is the better framework, there has not been a clear consensus.

We are living in an information-driven era. Millions of bytes of data get generated every second, and it becomes more challenging for organizations to keep vital information, software, and operating systems up and running on conventional in-house servers. That’s where cloud computing services come into play. 

Behind the success of any business, there lies a critical element and that is customer service, a vital factor that can make or break an entire enterprise. With everything being digitized in this new era, customers are no more dependent on the brand or company for services. Why? because they have a myriad of options to choose from for a single product or service. And if they aren't satisfied with the experience, they are just a click away from the next available option.

Big data adoption is increasing rapidly across organizations of all sizes. However, the method and distinction between obtaining Business Intelligence (BI) and employing Data Analytics (DA) to make actual business decisions with an impact are getting lost in translation. While both terms are used interchangeably, BI and DA are essentially distinct in many ways.

Personalized marketing campaigns are proven to drive performance and better customer outcomes. A Mckinsey report says that customers expect businesses they buy from to recognize them as individuals and their interests. 

Businesses around the world spend billions of dollars every year on cloud infrastructure services, storage, and computing resources. They can gain an edge over each other with effective cloud infrastructure without any wasteful cloud spending. There are two rising approaches towards cloud cost optimization today that they can choose from; Capital Expenditure (CapEx) and Operational Expenditure (OpEx).