Personalizing fashion with human-in-the-loop machine intelligence

While it seems that the definition of artificial intelligence is always evolving, it has remained fairly consistent that humans powered by machines outperform machines that work alone. Jay Wang and Jasmine Nettiksimmons discuss how this is true even as AI technologies make their way into helping consumers discover fashion and styles they love. Knowing where to place the human in the loop is critical and not easy, so Wang and Nettiksimmons give an inside look at how Stitch Fix systematizes collaboration between stylists and AI software to maximize their value creation together.


Machine Learning overtaking Big Data?

Is Machine Learning is overtaking Big Data?! We also examine trends for several more related and popular buzzwords, and see how BD, ML. Artificial Intelligence, Data Science, and Deep Learning rank.


Everyone knows that loops in R are to be avoided but vectorization is not always possible

It goes without saying that there are always many ways to solve a problem in R, but clearly some ways are better (for example, faster) than others. Recently, I found myself in a situation where I could not find a way to avoid using a loop, and I was immediately concerned, knowing that I would want this code to be flexible enough to run with a very large number of observations, possibly over many observations. Two tools immediately came to mind: data.table and Rcpp . This brief description explains the background of the simulation problem I was working on and walks through the evolution of ideas to address the problems I ran into when I tried to simulate a large number of individuals. In particular, when I tried to simulate a very large number of individuals, say over 1 million, running the simulation over night wasn’t enough.


Machine Learning. Forecasting Numeric Data with Multiple Linear Regression (Medical Expenses)

We will develop a forecasting example using multiple linear regression. The exercise was originally published in ‘Machine Learning in R’ by Brett Lantz, PACKT publishing 2015 (open source community experience destilled).


Real-time scoring with Microsoft R Server 9.1

Once you’ve built a predictive model, in many cases the next step is to operationalize the model: that is, generate predictions from the pre-trained model in real time. In this scenario, latency becomes the critical metric: new data typically become available a single row at a time, and it’s important to respond with that single prediction (or score) as quickly as possible. Consider the example of presenting your credit card for payment at a store: the bank has to evaluate that transaction as being fraudulent or not, and return that signal to the store while you wait for the credit card machine to respond. In order to minimize your waiting time, and considering that thousands of transactions may be processed each second, a response must be generated in milliseconds. Now, with Microsoft R Server 9.1, you can operationalize certain models trained using the RevoScaleR and MicrosoftML packages as real-time web services. These web services use the R objects you create when you train the model, but don’t themselves rely on an R interpreter. This means the latency of the service is very low, and can respond in a matter of milliseconds.


Updating R

As you might know by now, the latest R version was recently released (R 3.4.0). That means that you are highly encouraged to update your R installation. There are several ways to do this some of which are documented in these other blog posts: Tal Galili, 2013, Kris Eberwein, 2015. You would think that it’s just a matter of downloading the latest R installer for your OS, installing it, and continuing your analysis. The reality is a bit more complicated. The following short steps will make your life easier.


Implementing Successful Big Data and Data Science Strategy

Big Data and Data Science are two of the most exciting areas in the business today. While most of the decision makers understand the true potential of both the fields, companies remain skeptical on how to implement a successful big data strategy for their enterprises. This roadmap can help you in defining and implementing the right big data strategy in your organization. There are many ways to incorporate big data and data science process in your company’s operations, but the following practices outlined here would guide businesses make a perfect blueprint of their big data and implementation strategy.
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