Software Engineering Insights

The global technology ecosystem has an insatiable demand for AI today but without the right training data and efficient AI model development, this domain cannot be utilized to its full potential. With fully actualized AI models that arise from high-quality build lifecycles, continuous innovation in AI fields such as Natural Language Processing (NLP) or predictive analytics can be ensured.

If you’re leading the marketing campaigns of an organization, you could relate to this! The prospect data received from website forms, surveys, web browsing, a list of event attendees, or advertising is not enough to create opportunities and convert a lead. 

Taking a business to a global scale involves navigation, not just through untraversed markets but also across language-based hurdles. Natural Language Processing (NLP) offers the utility of Machine Translation, wherein a source language is converted to a target language automatically with significant accuracy. It is the most viable solution to a fast and effective translation process without human intervention.

Salesforce is the world’s leading CRM with over 150,00 customers that include businesses, non-profits, and institutions. The cloud-based CRM unites sales, marketing, eCommerce, service, IT, etc. under a roof, enabling businesses to drive growth. 

Every business needs to continuously find ways to utilize a workday to make the most of it. So, workflow automation in various business areas is the go-to alternative for a lot of enterprises. The Customer Relationship Management (CRM) workflow for businesses, handled by marketers, customer service executives, and sales reps, also derives cost and time efficiency-related benefits from automation.

In today's advanced competitive world, the most valuable commodity is data. Data has many forms, from pictures of people and places to patient records and banking transactions. Machine Learning is a branch of Artificial Intelligence that can fully leverage this data to learn from it and enhance a business' bottom line.

As a Chief Technology Officer (CTO) of an organization, who is either driving the technology team of a startup or leading IT operations in a megacorp; continuously innovating is the goal. 

If you’re leading the software engineering cycle of a product or a software development firm, then the struggle to get answers to the following questions must be constant.

The software development and delivery pipeline of DevOps, also referred to as the CI/CD pipeline, ensures that market-ready software products are released for public use faster. Continuous Testing is a set of evaluation procedures in the DevOps pipeline meant to ensure the product's quality and to enhance the reliability of the outcome.

Today, we are at a point in the software delivery paradigm where most IT enterprises have implemented DevOps in their workflow in some way, shape, or form. DevOps teams are guided by various operating principles, the most prominent of which are Continuous Integration (CI) and Continuous Delivery (CD), or CI/CD.

A modern infrastructure that’s agile, flexible, and scalable is on the mind of every infrastructure & operations (I&O) leader. In a cloud-first and automation world, I&O leaders are introducing new ways to utilize and manage infrastructure.