InfoQ Homepage Data Science Content on InfoQ
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.NET Interactive: Jupyter Notebooks with .NET Core - Preview 2
.NET Interactive is the new name for Try .Net. This suite of APIs and command-line tools allows you to create interactive notebooks and documents combining rich text, code and data. One of the main use cases for .NET Interactive is embedding code into a Jupyter Notebook.
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Oracle Cloud Now Offers Data Science and Machine Learning Services
Oracle recently announced the availability of its Cloud Data Science Platform, a native service on Oracle Cloud Infrastructure (OCI), which the software designed to let teams of data scientists collaborate on the development, deployment and maintenance of machine learning models.
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Michael Berthold on End-to-End Data Science Using KNIME Software
Open source data analytics platform KNIME CEO and co-founder Michael Berthold gave the keynote presentation at this year's KNIME Fall Summit 2019 Conference. He spoke about the end-to-end data science cycle. The data science process lifecycle mainly involves create and productionize categories.
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Data Science at the Intersection of Emerging Technologies
Kirk Borne, principal data scientist at Booz Allen Hamilton, gave a keynote presentation at this year’s Oracle Code One Conference on how the connection between emerging technologies, data, and machine learning are transforming data into value. Emerging technological innovations like AI, robotics, computer vision and more, are enabled by data and create value from data.
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Microsoft Announces .NET Support for Jupyter Notebooks
Earlier this month, Microsoft announced the public preview of .NET Core support to Jupyter Notebooks, allowing the use of code written in C# and F#. This release is part of the Try .NET project, an interactive documentation generator for .NET Core.
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Alternatives to RViz for Visualising Robotics Data Presented: Summary from ROSCon 2019
During ROSCon 2019 two interesting tools for visualising and interacting with ROS were demonstrated. The first tool which was demonstrated is Webviz, an online web-based replacement for RViz. Another interesting option which gives you more options for interaction is using Jupyter notebooks to visualise and interact with your robot.
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Microsoft Announces General Availability of Jupyter Notebooks Support for Cosmos DB
Recently Microsoft announced the general availability of Jupyter notebooks support for Cosmos DB, providing integrated support for running queries directly against all data models. As these notebooks run directly inside Cosmos DB; this allows for analyzing and visualizing the data directly from the Azure portal, without the need to extract the data.
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The First AI to Beat Pros in 6-Player Poker, Developed by Facebook and Carnegie Mellon
Facebook AI Research’s Noam Brown and Carnegie Mellon’s professor Tuomas Sandholm recently announced Pluribus, the first Artificial Intelligence program able to beat humans in 6 player hold-em poker. In the past years, computers have progressively improved, beating humans in checkers, chess, Go, and the Jeopardy TV show. Poker poses more challenges around information asymmetry and bluffing.
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Google Adds New Integrations for the What-If Tool on Their Cloud AI Platform
In a recent blog post, Google announced a new integration of the What-If tool, allowing data scientists to analyse models on their AI Platform – a code-based data science development environment. Customers can now use the What-If tool for their XGBoost and Scikit Learn models deployed on the AI Platform.
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Databricks MLflow Integration Now Generally Available
Databricks recently made MLflow integration with Databrick notebooks generally available for its data engineering and higher subscription tiers. The integration combines the features of MLflow with those of Databrick notebooks and jobs. MLflow provides the following three main capabilities: experiment tracking, projects, and MLflow models.
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Google Releases Google-Landmarks-V2, a Large-Scale Dataset for Landmark Recognition & Retrieval
Google has released Google-Landmarks-v2, an improved dataset for Landmark Recognition & Retrieval, along with Detect-to-Retrieve, a Tensorflow codebase for large-scale instance-level image recognition. Two companion Kaggle competitions based on Google-Landmarks-v2 were also launched. With over 200,000 landmarks in 5 million images, it is the largest landmark dataset ever published.