Despite decades of development, deep learning has been boosted by the increasing processing power of computers and the immense amount of data available, which has allowed it to obtain some achievements that were unthinkable years ago. Deep learning is at the heart of what we know today as artificial intelligence. However, like everything that is talked about a lot, deep learning has formed a series of myths that do not always obey reality.
Digital, network-connected systems are transforming every aspect of business — from your mission-critical workloads to your most rarely used applications. But the increases in scalability and cost efficiency come at a cost. Because every system is so reliant on network connectivity, unplanned downtime is becoming increasingly expensive.
Today we’ll talk more about the cost tracking for serverless architecture. Following the cost of serverless architecture utilization it might give you insights on how and what should you change or adapt to, to be able to reduce the cost. After all, we’re all always trying to improve our performances in every field while keeping the costs to a minimum (if possible).
In 2018, digital transformation ushered in a radical shift in how enterprises harness customer insights, technology capabilities, and rapid experimentation to drive revenue growth, profitability, and market leadership. Enterprises spent $1.3 trillion in 2018 on digital transformation technologies like public cloud platforms, microservices and containers, edge computing, machine learning, and artificial intelligence to improve customer experiences, business agility, and employee engagement.
If you’re a software developer, then you understand how vital application logging is in software development and a critical part of logging is something called logging levels. Log entries generally contain essential information—such as a timestamp, a message, and sometimes additional stuff like an exception’s stack trace. Those pieces of information are useful because they allow someone reading the log entry to understand how the application behaved in production.
The brand new Monitive platform is almost ready to be seen by the world, but just like any software development project, minor issues always surface that tend to push a release date back further and further. There’s always copy to be adjusted, links to be added, an icon that would improve clarity. Maybe a box is missing or the color of a button needs to change.
For our tech-minded readers, we strongly recommend you read the suggested articles in the US-CERT notification for detailed examples of how the hijacking takes place, for the rest of you, we’ve put together a summary of how the multifaceted attacks work.
A project post mortem is a lethal-sounding term that seeks to answer the question: did this project work? Was it worth the investment and the time, and if it wasn’t can you learn from it? As the term implies, the project must be “no more” or “ceased to be” or “bereft of life.” Creating this document requires a time investment, and it’s tempting to just move onto the next project.
In 2018, we launched many improvements to the Raygun Platform, including the biggest ever addition to Raygun: Application Performance Monitoring (APM). For this article, we wanted to surface our favorite releases from 2018, just in case you missed them.
