Systems | Development | Analytics | API | Testing

Savannah Whitman When your platform runs on APIs, all of those APIs need to run perfectly. Quickly resolving issues in your API isn’t just helpful, it’s mandatory. Latency and error monitoring are only the beginning: a healthy server isn’t the same thing as a healthy product. Resolving error cases and API abuse is easiest with full visibility into your API, which is where API analytics come in.

A new study from McKinsey warns that profit pools in the healthcare industry are likely to be flat due to the ongoing effects of the COVID-19 pandemic. To combat this stagnancy, the study identifies a number of opportunities for innovation in the industry.

Data is the true currency of the digital age, and it plays an indispensable role in defining and accelerating the mission of Government agencies. Every level of government is awash in data (both structured and unstructured) that is perpetually in motion. It is constantly generated – and always growing in volume – by an ever-growing range of sources, from IoT sensors and other connected devices at the edge to web and social media to video and more.

I don’t think anyone could argue that the last two years have changed the workplace forever. Entire nations have been confined to their homes during months of lockdowns, and the way we conduct business and interact with each other has passed a point of no return.

When businesses begin applying machine learning (ML) workflows to their use cases, it’s typically a manual and iterative process—each step in the ML workflow is executed until a suitably trained ML model is deployed to production.