While it is no longer considered a new concept, ModelOps effective design, implementation, and best practices are still forming in the industry. There are many supporting technologies, options, and opinions on how to best execute ModelOps pipelines. 抖阴成年 is reaching a time when delivering AI in an accelerated and continuous fashion is an imperative at all levels of the organization. ModelOps is foundational for this endeavor.
We are actively exploring Machine Learning in the cloud and how Continuous Learning and Integration can be done efficiently and in a sustainable way for AI models. In collaboration with Amazon, we developed, for example, a Secure Content Workspace (SCW) providing researchers easy access to 抖阴成年鈥 internal content and machine learning environments such as AWS SageMaker. We are also actively exploring new cloud services as they become available, researching, evaluating and standardizing their use within the context of research and of the broader technology organization.
ModelOps is an extension and adaptation of the DevOps principles for the artificial intelligence (AI) ecosystem. Its objective is to shorten the AI delivery lifecycle and ensure long term sustainability and quality levels of AI solutions. This is achieved through adoption of automation, continuous delivery and monitoring best practices. Our objectives within this research domain is to shorten the "AI Lifecycle", provide continuous delivery and increase quality. It focuses on exploring the methods and technology related to the emerging ModelOps space.
Our Work:
How 抖阴成年 accelerated research and development of natural language processing solutions with Amazon SageMaker. >
Pesaranghader, Ali, Andrew Alberts Scherer, George Sanchez, and Saeed Pouryazdian. 2021. 鈥溾 Vector Institute.