Operations | Monitoring | ITSM | DevOps | Cloud

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.

How much do Synthetics matter to your team? I think they matter a whole lot. Back when I was a freelance developer, I doubled my annual income with synthetics. Working mainly in database optimizations, I would finish out a contract and leave a synthetic monitor running at a very low frequency on their service. When I saw a pattern of slower performance, I knew it was time to hit the team lead-up to ask if I could help.

In a keynote at AI.Dev, Robert Nishihara (CEO, Anyscale) described the shift: A year ago, the people working with ML models were ML experts. Now, they’re developers. A year ago, the process was to experiment with building a model, then put a product on top of it. Now, it’s ship a product, find the market fit, then create customized models. The general-purpose generative AI models available to all of us today (such as ChatGPT) change the way work is done.

At Grafana Labs, open source has always been part of our DNA. But in 2023 — a year in which we reflected on 10 years of Grafana, among other major OSS milestones — the power of our open source community felt especially palpable.