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There has been growing interest in the capabilities of generative AI since the release of tools like ChatGPT, Google Bard, Amazon Large Language Models and Microsoft Bing. With the hype comes concerns about privacy, PII, security and, even more importantly, accuracy. And rightly so. Organizations are treading cautiously with their acceptance of generative AI tools, despite seeing them as a game changer.
Testing is still the most arduous, painful, and expensive task within a DevOps practice, regardless of framework or approach. Why? Because the current approaches to testing and development are not focused on production. Production-Driven Development (PDD), allows for rapid iteration without sacrificing stability or confidence. Following PDD, a small team or single developer can launch an application in weeks that used to take multiple teams months or a year.
The world of AI and machine learning has evolved at an accelerated pace these past few years, and the advent of ChatGPT, DALL-E, and Stable Diffusion has brought a lot of additional attention to the topic. Being aware of this, Grafana Labs prepared an integration for monitoring one of the most used machine learning model servers available: TensorFlow Serving. TensorFlow Serving is an open source, flexible serving system built to support the use of machine learning models at scale.
Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week we are exploring how generative AI can change the future of healthcare. Step into the world of generative AI, the fascinating field of AI that’s got everyone in the healthcare industry buzzing.
