The rapid advancement of large language models (LLMs) like ChatGPT has captured headlines and imaginations. AI systems can now generate amazingly human-like text on any topic with just a few prompts.These behemoths, with their unparalleled capabilities, have necessitated a reevaluation of governance models. As organizations explore integrating LLMs into business operations, it’s crucial to implement governance measures enabling innovation while managing risks. As executives, understanding the transition from traditional machine learning governance to LLM-centric AI governance is crucial.
This is great, thanks for sharing. Key component in evolving FM based applications is making them feel as deterministic as possible vs probabilistic. Framework like this would enable generating trust in the outputs of these FMs.. exciting.
hi all creator here. We built this version of our product focused on dynamic data science teams that just wanted to be able to deploy, scale and run their models without worrying about ops. Some more details:
It's not used for private models. Its meant to make sure if someone builds an application based on your models you won't yank that version from under them. If you never expose your model (for profit) you can delete at will and we have no rights.
Algorithmia here . What are you concerned about license wise? You own all ip always. There is some restrictions if you choose to commercialize on our service (mostly guarantee you won't take it down on users). System was built for this. Happy to answer questions
Algorithmia is "DevOps for AI". The Algorithmia engineering team is responsible for building the Algorithmia platform, a highly-available distributed platform that runs the public algorithm marketplace on Algorithmia.com as well as large AI/ML workloads for our enterprise customers. Platform engineers build features and work with cloud providers to ensure our system is the most stable, highly-available, and feature-rich platform for AI/ML. Our stack includes Kubernetes, Docker, and various Java (Scala) microservices and backing stores.
this doesn't solve the biggest problem with multi-cloud which is data egress though. Even if latency is acceptable you would get killed on data transfer for any significant amount of data.