Day: July 5, 2019

John Mount has an interesting post looking at replicating linear models without training data:

Let’s work an example in R. Suppose we are working with a linear regression model and from our donor system we have extracted the following representation of the model as “intercept” and “betas”.

intercept <- 3 betas <- c(weight = 2, height = 4)

Our goal is to build a linear regression model that has the above coefficients. The way we are going to do this is by building our own synthetic data set such that the regression fit through this data set yields these coefficients.

It’s fairly straightforward to do this for linear models; as things get more complicated, however, the difficulty level spikes.

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David Smith has a review of Microsoft’s R roadmap, focusing on Azure:

The post references this guide to the machine learning services in Azure, along with their supported languages. Services that currently support R include Azure Machine Learning Studio, SQL Server Microsoft Machine Learning Service, Microsoft Machine Learning Server, Azure Data Science Virtual Machine, Azure Databricks, and more.

David links to this strategy post:

The R and Python programming languages are primary citizens for data science on the Azure AI Platform. These are the most common languages for performing data preparation, transformation, training and operationalization of machine learning models; the core components for one’s digital transformation leveraging AI. Yet they are fundamentally different in many aspects, directly affecting not only deployed solutions IT architectures but also but also corporate strategies for developer skills and product supportability.
 
This series of articles is designed help you understand the options your company and customers have to support and evolve their R strategy.

It’s good to see some of this out in the open for planning purposes.

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