Systems | Development | Analytics | API | Testing

Artificial Intelligence (AI) is here, and it has the potential to revolutionize industries, enhance customer experiences, and drive business efficiencies. But with great power comes great responsibility — ensuring that AI use is ethical is paramount to building and maintaining customer trust. At Tricentis, we’re committed to responsible AI practices. At the core of this commitment are data privacy, continuous improvement, and accessible design.

The modern data-driven approach comes with a host of benefits. A few major ones include better insights, more informed decision-making, and less reliance on guesswork. However, some undesirable scenarios can occur in the process of generating, accumulating, and analyzing data. One such scenario involves organizational data scattered across multiple storage locations. In such instances, each department’s data often ends up siloed and largely unusable by other teams.

With organizations prioritizing data-driven decision-making, the amount of collected and stored data is reaching historic highs. Meanwhile, organizations are democratizing access across all functions to convert this data into actionable insights. Since more users will work with sensitive data, ensuring secure access is more important than ever. Organizations must regulate and maintain the relationship between their data assets and users. Why?

Performance testing is a form of software testing that focuses on how a system running the system performs under a particular load. This type of test is not about finding software bugs or defects. Different performance testing types measures according to benchmarks and standards. Performance testing gives developers the diagnostic information they need to eliminate bottlenecks. In this article you will learn about.

As compute gets increasingly powerful, the fact of the matter is: most AI workloads do not require the entire capacity of a single GPU. Computing power required across the model development lifecycle looks like a normal bell curve – with some compute required for data processing and ingestion, maximum firepower for model training and fine-tuning, and stepped-down requirements for ongoing inference.

Data is the driving force of the world’s modern economies, but data teams are struggling to meet demand to support generative AI (GenAI), including rapid data volume growth and the increasing complexity of data pipelines. More than 88% of software engineers, data scientists, and SQL analysts surveyed say they are turning to AI for more effective bug-fixing and troubleshooting. And 84% of engineers who use AI said it frees up their time to focus on high-value activities.

How to leverage Fivetran and Starburst to supercharge your data lakehouse with end-to-end data pipelines.

In today's fast-paced world of media publishing, keeping up with technological advancements and changing consumer preferences is no easy task. Tight budgets, fierce competition and evolving audience behaviors add to the pressure, creating what's often termed the "content crash" — a saturation of content that makes it hard for publishers to stand out. But amidst these challenges, there's a beacon of hope: generative AI.

On July 19th, 2024, the world witnessed a large-scale computer outage caused by a faulty update from cybersecurity giant CrowdStrike. This incident, affecting millions of Windows devices globally, serves as a stark reminder of the domino effect that software errors can have. Since then, CrowdStrike and other industry experts have shared their preliminary incident report in which they outline the incident and the steps they will take to prevent future issues like this.

On July 19th, 2024, the world witnessed a large-scale computer outage caused by a faulty update from cybersecurity giant CrowdStrike. This incident, affecting millions of Windows devices globally, serves as a stark reminder of the domino effect that software errors can have. In part one of this series, we discussed the role QA methodologies can play in preventing future outages.