Systems | Development | Analytics | API | Testing

Artificial Intelligence (AI) technology has been transforming industries and our day-to-day lives alike. Its undeniable impact has led to significant effort and investment into making AI more accessible to everyone, everywhere. Open-source AI technology and AI APIs are two examples of our commitment to AI democratization. AI APIs democratize AI by providing access to pre-trained AI models, even for developers without extensive machine learning expertise.

You’ve built an incredible AI API and are ready to release this functionality to your users. The issue is that you’re not sure exactly how to monetize it. Generally, monetizing APIs is challenging at scale, but monetizing AI APIs can be even more difficult. Some AI APIs may be charged on a “per API call” basis, but many AI APIs require charging users for input and output tokens used within an API call. Others may charge per unique user or API key.

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.

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.

Imagine that your application is working as if it has no issues at all. This makes customers happy, and you are ready to launch a new feature in the next update. But with the arrival of a new update and new code, everything breaks! It does not only mean that a new feature does not work as intended but also that all functions cease to function correctly. Your team quickly looks for the errors and corrects them, often resulting in increased time, extra costs, and a drop in your company’s credibility.

In the fast-paced world of government software development, efficiency and effectiveness are paramount. One way government organizations enhance agility, foster innovation, and optimize resources in the public sector is through citizen development—the practice of allowing non-technical employees to create their own software applications using low-code or no-code platforms.

Data modernization transforms an organization’s data into more usable and accessible formats, making it usable by modern applications and systems, such as cloud-native applications, IoT devices, and mobile applications. Data modernization also includes extracting, cleaning, and migrating the data into advanced platforms.

Process orchestration, also called business process orchestration, is an approach to manage and organize multiple automated tasks or workflows within a business process, like ETL process orchestration. It involves coordinating data extraction, transformation, and loading tasks across various systems to create a unified data source.