Like everyone else in the world, we are thinking hard about how we can harness the power of AI and machine learning while also staying true to our core values around respecting the security and privacy of our users’ data. If you use Sentry, you might have seen our “Suggested Fix” button which uses GPT-3.5 to try to explain and resolve a problem. We have additional ideas being developed as well that we’re excited to preview.
As the demand for AI-based solutions continues to rise, there’s a growing need to build machine learning pipelines quickly without sacrificing quality or reliability. However, since data scientists, software engineers, and operations engineers use specialized tools specific to their fields, synchronizing their workflows to create optimized ML pipelines is challenging.
In the dynamic world of IT, traditional network monitoring approaches are no longer sufficient to manage the complexities of today’s networks—be they wired or wireless. To stay ahead of network events, IT administrators must shift from being reactive to adopting a proactive stance. This transition involves a comprehensive approach to network monitoring that includes forecasting future network requirements with the help of machine learning (ML) technology.
The advent of Machine Learning (ML) has unlocked new possibilities in various domains, including full lifecycle Application Performance Monitoring (APM). Maintaining peak performance and seamless user experiences poses significant challenges with the diversity of modern applications. So where and how does ML and APM fit together? Traditional monitoring methods are often reactive, resolving concerns after the process already affected the application’s performance.
In a traditional DevOps implementation, you automate the build, test, release, and deploy process by setting up a CI/CD workflow that runs whenever a change is committed to a code repository. This approach is also useful in MLOps: If you make changes to your machine learning logic in your code, it can trigger your workflow. But what about changes that happen outside of your code repository?
