Software Engineering Insights

Artificial Intelligence (AI) and data engineering are closely interlinked. On one hand, making sense of unstructured data is the process known as data science or data engineering. On the other side of the same coin, AI-programmed computers have the ability to learn as they go, getting better at solving particular sorts of problems as they accumulate more data. So one cannot exist without the other.

Machine Learning (ML) is an application of Artificial Intelligence that has the maximum number of use cases, in almost every industry. Healthcare, automobile, marketing, finance, agriculture, retail- all of them are leveraging the power of ML to automate tasks and bring agility to operations. 

Migrating to the cloud is the new normal for businesses of all sizes. Based on availability, compliance, performance, sovereignty, and other technical factors, businesses are making a choice for a relevant cloud services provider. 

In an SDLC, there are several testing frameworks that help to assess if the application is progressing in the right direction. It could be in terms of usability, security, compatibility, performance, integrations, etc.

Speech recognition is a technology that has been going through continuous innovation and improvements for almost half a century. It has led to several successful use cases in the form of voice assistants such as Alexa, Siri, etc., voice biometrics, official transcription software, and the list goes on. So what really is Automatic Speech Recognition and what are the underlying technologies that enable it?

The business landscape of the modern world is heavily customer-centric with marketing, sales, and customer support at the center of operations. Salesforce is the most sought-after Customer Relationship Management (CRM) software intended for these purposes. There are a variety of roles and responsibilities in an enterprise's Salesforce team and the Salesforce Administrator is usually at the helm of it.

Increasingly today, Financial Technology (FinTech) companies have been using mobile devices and applications as promotional platforms. Biometrics technologies are a necessary addition to the FinTech domain and have the critical task of enhancing security through the accurate identification and verification of the customer's identity.

A majority of healthcare data today, about 80% of it, happens to be unstructured. Most sources used for aggregating Electronic Health Records (EHR) require a considerable amount of pre-processing because most of them are not machine-readable. Natural Language Processing (NLP) and various Machine Learning (ML) techniques can, however, unlock all the potential enmeshed in unstructured data.

When it comes to seeking app-based medical solutions, physical health is often given precedence over cognitive or mental health. However, millions of people suffer silently from unseen, yet debilitating effects of mental illnesses. That is the pain area that the creators of the research-backed mental wellness app Wysa have chosen to address. 

Artificial Intelligence, in practice, is complex. There are several technologies and training methodologies involved. There is a high risk involved in algorithm failure. With this,  businesses want nothing but the best of AI practitioners when it comes to executing the use of AI technology. That’s when the Artificial Intelligence (AI) Center of Excellence (CoE) comes into the picture.