Sun.Dec 01, 2019

6 Challenging Open Source Data Science Projects to Make you a Better Data Scientist

Analytics Vidhya

Overview Here are 6 challenging open-source data science projects to level up your data scientist skillset There are some intriguing data science projects, including. The post 6 Challenging Open Source Data Science Projects to Make you a Better Data Scientist appeared first on Analytics Vidhya.

Data analytics helps Sistema ‘klip-IT’ on production efficiencies

Phocas

Sistema means ‘system’ in Italian and also signifies the company’s systematic approach to manufacturing,which has helped the New Zealand business take on the world. A leading manufacturer of food storage containers, Sistema’s success is two-fold.

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4 Industries Shaken By The Artificial Intelligence Revolution

Smart Data Collective

Artificial intelligence has had a profound impact on our lives. A study by Tractica found that the global AI market is projected to grow to $118.6 billion within the next six years. The market for artificial intelligence technology is growing largely due to the number of industries that depend on it.

Data Drift Detection for Image Classifiers

Domino Data Lab

This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production. Run the example in a complementary Domino project. Introduction: preventing silent model degradation in production.

Data Analytics in the Cloud for Developers and Founders

Speaker: Javier Ramírez, Senior AWS Developer Advocate, AWS

You have lots of data, and you are probably thinking of using the cloud to analyze it. But how will you move data into the cloud? In which format? How will you validate and prepare the data? What about streaming data? Can data scientists discover and use the data? Can business people create reports via drag and drop? Can operations monitor what’s going on? Will the data lake scale when you have twice as much data? Is your data secure? In this session, we address common pitfalls of building data lakes and show how AWS can help you manage data and analytics more efficiently.