Iris AI
Enago Read
ScienceBox
FirstIgnite
Receptiviti
genei.io
arc.io
Narrative Science
Deepnote
Amazon SageMaker
Apache Zeppelin
Saturn Cloud
Databricks Unified Analytics Platform
Google BigQuery
Azure Synapse Analytics
GeoSpock
The Iris.ai Researcher Workspace is a flexible tool suite that allows all researchers - without a necessary AI background knowledge - to approach a project in a variety of ways. Modules include content based explorative search, machine analysis of document sets, extracting and systematizing data points, automatically writing summaries of multiple documents - and very powerful filters based on context descriptions, the machine’s analysis, or specific data points or entities. The Iris.ai engine for scientific text understanding is a powerful interdisciplinary system that can be automatically reinforced on a specific research field for much more nuanced machine understanding - without human training or annotation.
The Iris.ai Researcher Workspace can service numerous research use cases, from knowledge processing in R&D, systematic literature reviews and IP analysis to automated post-market surveillance or pharmacovigilance. Let AI take over all those tedious tasks so our best and brightest can focus on the tasks that really matter and improve our lives.
DeepnoteBased on our record, Deepnote seems to be more popular. It has been mentiond 32 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Deepnote - A new data science notebook. Jupyter is compatible with real-time collaboration and running in the cloud. The free tier includes unlimited personal projects, up to 750 hours of standard hardware, and teams with up to 3 editors. - Source: dev.to / 5 months ago
We looked into many of these issues with Deepnote (YC S19) [https://deepnote.com/]. What we found is that these are not necessarily problems of the underlying medium (a notebook), but more of the specific implementation (Jupyter). We've seen a lot of progress in the Jupyter ecosystem, but unfortunately almost none in the areas you mentioned. - Source: Hacker News / about 1 year ago
Upload your ipynb to Deepnote and publish as an app. That simple. https://deepnote.com. - Source: Hacker News / about 1 year ago
Using Deepnote, we'll create a Python notebook and upload the two GeoJSON files into a data directory. - Source: dev.to / over 1 year ago
Deepnote - A new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud. Free tier includes unlimited personal projects, up to 750 hours of standard hardware and teams with up to 3 editors. - Source: dev.to / over 1 year ago
Enago Read - All In One AI-Powered Reading Assistant. A Reading Space to Ideate, Create Knowledge and Collaborate on Research
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
ScienceBox - Simple data science collaboration & productivity on the web
Apache Zeppelin - A web-based notebook that enables interactive data analytics.
FirstIgnite - Matching scientific research to business needs
Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.