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.
Improve traceability, auditability and collaboration in your data science teams: Learn effective version control practices that work for your whole data science team - partnering with CurveNote to provide new capabilities to gain insights and collaboration throughout your teams
Collaborate around Jupyter notebooks, and share key-insights: Learn how sharing and remixing technical content that is linked to the source can remove organizational/communication barriers. This allows non-technical stakeholders, analysts, and data teams to more easily interact while staying in their preferred context and eliminating copy/paste and “communication chaos."
Receive reports that are up to date, and can be readily updated: Reduce time on creating updates, building reports, and asking what-if’s? Warm project restarts. Consume/deliver reliable up-to-date information that you can trust based on a linked, auditable reporting process.
Gain insights into in-progress work and shorten feedback cycles: Supercharge your model review processes, and get feedback to your team directly in the environment in which they are already working and can action it.
Demo
Open Q&A
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.
Improve traceability, auditability and collaboration in your data science teams: Learn effective version control practices that work for your whole data science team - partnering with CurveNote to provide new capabilities to gain insights and collaboration throughout your teams
Collaborate around Jupyter notebooks, and share key-insights: Learn how sharing and remixing technical content that is linked to the source can remove organizational/communication barriers. This allows non-technical stakeholders, analysts, and data teams to more easily interact while staying in their preferred context and eliminating copy/paste and “communication chaos."
Receive reports that are up to date, and can be readily updated: Reduce time on creating updates, building reports, and asking what-if’s? Warm project restarts. Consume/deliver reliable up-to-date information that you can trust based on a linked, auditable reporting process.
Gain insights into in-progress work and shorten feedback cycles: Supercharge your model review processes, and get feedback to your team directly in the environment in which they are already working and can action it.
Demo
Open Q&A
