It’s just not December until the “best of” or “top 10” posts start getting compiled and published.

Here’s a great overview the year’s tops AI and Data BigTech announcements.

The year 2019 was a very productive year in terms of new updates launched by tech giants. From new operating systems to open sourcing frameworks, this year was a giant step towards white boxing technologies and data democratisation.

In this article, we are compiling a list of all the major announcements in this field:

This epsisode of the AI Show talks about the new ML assisted data labeling capability in Azure Machine Learning Studio.

You can create a data labeling project and either label the data yourself, or take help of other domain experts to create labels for you. Multiple labelers can use browser based labeling tools and work in parallel.

As human labelers create labels, an ML model is trained in the background and its output is used to accelerate the data labeling workflow in various ways such as active learning, task clustering, and pre-labeling. Finally, you can export the labels in different formats.

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This video posted by Microsoft has an interesting look at the challenges holding companies back from digital transformation in the cloud era.

What if we could store data in such a way that we never had to worry about quality loss or degradation? Microsoft Partner Deputy Lab Director Ant Rowstron shares his thoughts on redesigning the datacenter for a cloud-centric world. An ordinary piece of glass could have an extraordinary impact on the ways in which we store data and preserve collective memories. Using silica glass presents an energy efficient, sustainable and nearly indestructible method of data storage that could change the way we preserve our history, movies and music and more.

Discover more about this story at Microsoft Innovation: http://msft.social/b1iM95

Julie Lerman shares some insight and great tips for working with Entity Framework in Docker. You will see thing tips for working with the SQL Server Docker image, using environment variables for passports, using the Docker tools for Visual Studio and so much more!

Useful Links

Wall Street Journal explores the future of satellite internet.

The most reliable streaming providers have typically used cable to deliver content. But that’s all changing with the launch of new and better satellites that could one day give us 5G, low latency data. The Wall Street Journal speaks with the chief of the International Bureau at the FCC to discover how those changes are happening almost overnight.

Here’s an interesting article on how to represent a categorical feature, with 100’s of levels, in a model in R.

In this post, we will discuss using an embedding matrix as an alternative to using one-hot encoded categorical features for in modeling. We usually find references to embedding matrices in natural language processing applications but they may also be used on tabular data. An embedding matrix replaces the spares one-hot encoded matrix with an array of vectors where each vector represents some level of the feature. Using an embedding matrix can greatly reduce the memory needed to handle the categorical features.