Data science teams today use and deploy a broad stack of proprietary and open source software in their ML and analytics pipelines. The single most effective element in this stack is Jupyter Notebooks, which enable rapid prototyping, interactive computing, and flexible deployment and infrastructure choices. Supporting this exploratory research in Jupyter and integrating it into your company’s practices is still a struggle many teams face, especially around sharing key-insights, knowledge/information management, and rapid feedback between data science teams and their stakeholders.
During this webinar, we’ll dive into these collaborative and organizational challenges in the context of Data Science teams working in retail fraud. We’ll see how CurveNote can provide your technical teams with essential versioning and tracking capabilities and close the gap between your technical and non-technical staff. The CurveNote platform enables effective, real-time communication and contribution across your organization. How? - by providing a platform to easily share, remix and create linked content allowing different groups to collaborate in a familiar context by hand crafting reports, preparing review material for a meeting or analyzing the outputs of automated reports.
