In recent years, machine learning operations (MLOps) have become the standard practice for developing, deploying, and managing machine learning models. MLOps standardizes processes and workflows for faster, scalable, and risk-free model deployment, centralizing model management, automating CI/CD for deployment, providing continuous monitoring, and ensuring governance and release best practices.

Over the last decade, many IT leaders have felt confident they were ready for the API evolution. Afterall, they invested a lot into it. Traditional API management platforms promised to deliver on transformational challenges, and offer cornerstone capabilities – API cataloguing, specification support, authentication, authorization permissions, security, policy management, and even developer portals.

Organizations are generating more data than ever before, but accessing it and gaining insights from it can be challenging. Fivetran and Snowflake simplify and speed up the process.

Organizations generate vast amounts of data daily during various business operations. For example, whenever a customer checks out out at a retail outlet, data such as the customer identifier, retail outlet identifier, time of check out, list of purchased items, and the total sales value can be captured in the Point of Sales (PoS) system. Similarly, field sales staff may record possible sales opportunities in spreadsheets.

The problem with test data is that it can become stale very quickly. This is either through its use from testing or from the fact that it is naturally aging in the test environments. This is not just an issue for performance testing, although the volumes of data sometimes required for performance testing do make it harder. This also affects functional testing as well as batch testing and business acceptance testing amongst others.

Imagine being at the helm of a project, excited to launch an innovative software application, but then... the testing phase reveals critical flaws that could have been caught earlier. This experience is a stark reminder of how vital a cohesive test plan can be. Join me as we explore how a visual test plan builder can transform your approach to crafting robust test plans, ensuring quality at every step of the way!

Testing web applications is more difficult today than ever before, as testing on the web is time-consuming. Complexity increases with technology, and businesses and developers need efficient tools to deal with it. This is where Selenium became one of the most widely used test automation tools and has been crucial in reducing testing efforts as well as improving user experience. Selenium is an open-source tool developed to automate web browsers.

Sidekiq is one of the most popular open-source background job libraries for Ruby. As one of ActiveJob's most popular backends, it's often used to run asynchronous jobs in Rails applications. It leans on Redis to manage queues and jobs, which makes it fast. Developers can run background jobs using Sidekiq with or without ActiveJob, and we'll explore both in this article. Just as useful - Sidekiq scheduled jobs allow you to run a job after a given amount of time or at a given time.

Are self-driving cars safe? The software in modern cars contains more than 100 million lines of code that enable many different features — cruise control, speed assistance, and parking cameras. And, the code within these embedded systems only gets more complex. This trend will continue as cars of the future become more connected. Cars are increasingly dependent on technology. And they will progressively become more autonomous — and ultimately self-driving.

Artificial Intelligence (AI) has become an inseparable part of our lives, revolutionizing industries, transforming workspaces, and influencing how we interact with technology. From virtual assistants like Siri and Alexa to advanced machine learning algorithms driving breakthroughs in medicine, AI is everywhere. But as AI continues to evolve, a growing question lingers: Is AI making us smarter or more dependent?