There is a growing recognition that insurers can introduce data, analytics and AI into virtually all of the important insurance functions and workflows, including product development, pricing and risk selection, underwriting, claims management, contact center optimization, distribution management, reinsurance, and understanding and shaping customer journeys. Here are some of the exciting ways insurance companies can put data to work.

Shaping the future of AI goes hand in hand with discussions of diversity, equity, inclusion (DE&I), and women’s leadership. As AI increasingly pervades, revolutionizes industries, and reshapes the daily lives of people, the question of addressing intrinsic biases and ensuring inclusivity in the development of AI becomes critical.

Accelerate data movement into the Databricks Data Intelligence Platform to execute analytic workloads, AI and GenAI.

Feedback loops are key to what we do as developers. They inform us and allow us to learn from our users’ past and present behaviors. This means we can work toward proactive development based on our reactive past. Proactive Programming vs. Reactive Programming Proactive programming pushes the concept of isolating variables that might be easy to mitigate. The idea is to eliminate problems before they become problems.

With an ever-growing user base of approximately 6.7 billion active smartphone users globally, mobile apps have become an integral part of our modern existence and digital transformation. Recognizing their omnipresence and revenue-generating potential, it becomes imperative not to overlook their significance. Nevertheless, one of the initial queries posed by clients while presenting their app concepts to us revolves around understanding the app development cost.

We all know the why it’s important to write clean, reliable code. But let’s face it, catching bugs before they wreak havoc on our applications can feel like chasing after a greased weasel. That’s where testing frameworks come in, acting as our trusty bug-hunting companions. In this blog, we’ll be putting two popular frameworks – Jasmine and Mocha – head-to-head.

As can be inferred from their name, foundation models are the foundation upon which developers build AI applications for tasks like language translation, text summarization, sentiment analysis and more. Models such as OpenAI's GPT, Google's Gemini, Meta’s Llama and Anthropic’s Claude, are pre-trained on vast amounts of text data and have the capability to understand and generate human-like language.