Notes de Francis

https://azure.microsoft.com/en-us/updates/azure-cognitive-services-translator-canadian-french-language-text-translation-now-available/

https://arxiv.org/abs/2010.10442

https://github.com/e-commerce-search/bert2dnn

https://github.com/adamerose/pandasgui

How to create and deploy a model card in the cloud with Scikit-Learn

https://cloud.google.com/blog/products/ai-machine-learning/create-a-model-card-with-scikit-learn

https://github.com/pytorch/fairseq/tree/master/examples/m2m_100

https://www.inc.com/soren-kaplan/why-no-code-low-code-software-is-industry-disruptor-you-should-pay-attention-to.html

https://chrome.google.com/webstore/detail/find-code-for-research-pa/aikkeehnlfpamidigaffhfmgbkdeheil

https://www.datamechanics.co/blog-post/spark-and-docker-your-spark-development-cycle-just-got-ten-times-faster

Spark and Docker: Your Spark development cycle just got 10x faster ! – Data Mechanics Blog Native support for Docker is in fact one of the main reasons companies choose to deploy Spark on top of Kubernetes instead of YARN. In this article, we will illustrate the benefits of Docker for Apache Spark by going through the end-to-end development cycle used by many of our users at Data Mechanics. www.datamechanics.co

https://www.freecodecamp.org/news/scalable-data-analytics-pipeline/

How to Build a Scalable Data Analytics Pipeline As the data keeps growing in volume the data analytics pipelines have to be scalable to adapt the rate of change. And for this reason, choosing to set up a the pipeline in cloud makes perfect sense (since cloud offers on-demand scalability and flexibility). In this article I will demystify how to build a scalable and adaptable data processing pipeline in Google Cloud. www.freecodecamp.org