Daily Newsletter — 17th December 2020

Published in

Jovian

3 min readDec 17, 2020

Auto Summarizer Extention for Python docstring using CodeBERT, instructions on how to implement AI successfully and a Custom Visualizations for Machine Learning in today’s Data Science Daily 📰

Auto Summarizer for Python docstring using CodeBERT (VS Code)

This Visual Studio Code extension quickly generates docstrings for python functions using Artificial Intelligence with Natural Language Processing technology. This project is forked for autoDocstring. Previously, the description of the function had to be written by the user, but here the AI summarizes the code.

Features

AI Quickly generate a docstring snippet that can be tabbed through.
Choose between several different types of docstring formats.
Infers parameter types through pep484 type hints, default values, and var names.
Support for args, kwargs, decorators, errors, and parameter types

Docstring Formats

Google (default)
docBlockr
Numpy
Sphinx
PEP0257 (coming soon)

Install here: https://marketplace.visualstudio.com/items?itemName=graykode.ai-docstring

GitHub: https://github.com/graykode/ai-docstring

This project is licensed under the Apache 2.0 License which is based on MIT License.

How to implement AI successfully

Jonathan Weinberg writes about making solid data strategies and implementing them to ensure AI runs successfully. He believes that siloed data stores severely restrict AI’s ability to influence the digital ecosystem around it, rendering it little more than an expensive brain in a box

He also elaborates on how business decisions and processes must adapt learn to adapt to ensure a healthy workflow and clean, high-quality data critical to AI projects. An AI requires an organisation to have infrastructure, process and people in place before embarking on any serious project.

Jonathan Weinberg is a freelance journalist, writer and media consultant/trainer specialising in technology, business, social impact and the future of work and society.

Article: https://www.raconteur.net/technology/artificial-intelligence/ai-implementation-data/

Panels: Custom Visualizations for Machine Learning

Comet Panels, which has been under development and testing for the past year. Starting today, you can tap into the ecosystems of JS/HTML/CSS and create new widgets with custom styles and behaviours, or add new visualizations and chart types.

The key principles in designing Comet Panels were the following:
1. Dynamic — Panels should be dynamic and update on new experiments and results when they arrive. There’s nothing worse than a stale and misleading visualization.
2. Flexible — Users should be able to build and customize anything they want without the limitations of a GUI or a specific dependency
3. Reusable — teammates and community members should be able to share and reuse each other’s panels.

Blog: https://www.comet.ml/site/introducing-panels-custom-visualizations-for-machine-learning

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