“We build our solution on top of an AWS stack. It frees us up to think about creating innovative solutions and not worry about infrastructure”- Vu Lam, CEO of Katalon In order to help enterprise customers release faster and deliver on time, Katalon has built an integration where automation scripts can be stored in AWS CodeCommit and an AWS CodePipeline project can execute them using the integration.

When faced with new innovations like GenAI, every company must avoid pitfalls that result in unscalable and unsustainable solutions.

The journey from a great idea for a Generative AI use case to deploying it in a production environment often resembles navigating a maze. Every turn presents new challenges—whether it’s technical hurdles, security concerns, or shifting priorities—that can stall progress or even force you to start over.

For the last 10+ years, Docker has been one of the leading technology tools for helping developers build, run, update, and manage containers. It is one of the most widely used containerization tools available, able to work with Linux, Microsoft Windows, Apple MacOS, and all of the leading cloud service providers. With over 20 million monthly users, you’ve likely used Docker before, whether for your own projects or as part of a larger team.

How are AI governance and data governance related? Better still, what’s more important for an organization to focus on, AI-powered data governance or AI data governance? These are important questions, but before we answer these, let’s understand how AI and data governance are related to each other.

According to Stack Overflow’s recent survey, 62% of developers share a common concern – a growing and never-ending technical debt. Arpita Goala , Content Marketing Manager September 26th, 2024 Technical debt or tech debt is often an unintended and unavoidable consequence of software development. With the speed at which technologies and products evolve, technical debt is something that every quality assurance (QA) team must deal with.

While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.