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Category: Machine Learning

Distributed Model Training with Dask and SciKit-Learn

Matthieu Lamairesse shows us how we can use Dask to perform distributed ML model training:

Dask is an open-source parallel computing framework written natively in Python (initially released 2014). It has a significant following and support largely due to its good integration with the popular Python ML ecosystem triumvirate that is NumPy, Pandas and Scikit-learn. 

Why Dask over other distributed machine learning frameworks? 

In the context of this article it’s about Dask’s tight integration with Sckit-learn’s JobLib parallel computing library that allows us to distribute Scikit-learn code with (almost) no code change, making it a very interesting framework to accelerate ML training. 

Click through for an interesting article and an example of using this on Cloudera’s ML platform.

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Using Pre-Trained Sentiment Models with Power BI

Ryan Wade shows us how to use a pre-built sentiment analysis model with Power BI:

As of this writing, there are two pre-trained models available: one for sentiment analysis and another for image classification. This example focuses on sentiment analysis.

Both of these installations are freely available to the on-prem version of SQL Server 2017 and later. For more information on how to install these on your instance, reference this article for SQL Server Machine Learning Services and this article for pre-trained models.

Click through for step-by-step instructions.

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The Hype Cycle for Artificial Intelligence

William Vorhies takes a look at Gartner’s hype cycle for AI (among other things):

Supposing you’re a business leader and supposing you’re trying to make an intelligent decision about prioritizing your AI adoption plans.  It’s likely that like many of us the first thing you’d reach for would be one of Gartner’s many hype cycle or magic quadrant analyses.

What you might not know is that you now need an expert just to guide you through the expert literature.  There has been such a proliferation of hype cycles and magic quadrants that you could easily be looking in the wrong place.

The hype cycle is definitely opinion-based, but I think it’s a useful look at the relative maturity of different segments of an industry or technology cluster. Do read the whole thing, though, as these things aren’t perfect.

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Security Changes in ML Services

Dennes Torres goes over some of the security changes with Machine Learning Services in SQL Server 2019:

I have a confession to make. Why, in my last article about shortest_path in SQL Server 2019, have I used Gephi in order to illustrate the relationships, instead of using a script in for the same purpose and demonstrate Machine Learning Services as well?

The initial plan was to use an R script; however, the R script which works perfectly in SQL Server 2017 doesn’t work in SQL Server 2019.

The change is a positive one from the standpoint of security, but it also makes life more difficult. I found this particularly tricky when installing TensorFlow and Keras in R via ML Services.

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Explaining Black Box Models with LIME

Holger von Jouanne-Diedrich takes us through the intuition of LIME:

There is a new hot area of research to make black-box models interpretable, called Explainable Artificial Intelligence (XAI), if you want to gain some intuition on one such approach (called LIME), read on!

Before we dive right into it it is important to point out when and why you would need interpretability of an AI. While it might be a desirable goal in itself it is not necessary in many fields, at least not for users of an AI, e.g. with text translation, character and speech recognition it is not that important why they do what they do but simply that they work.

In other areas, like medical applications (determining whether tissue is malignant), financial applications (granting a loan to a customer) or applications in the criminal-justice system (gauging the risk of recidivism) it is of the utmost importance (and sometimes even required by law) to know why the machine arrived at its conclusions.

One approach to make AI models explainable is called LIME for Local Interpretable Model-Agnostic Explanations. There is already a lot in this name!

LIME is not trivial to use and it can be very slow, but it is a great way to visualize models.

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Concepts in Support Vector Machines

Abhijit Telang takes us through the calculations involved in Support Vector Machines and then gives us an example in R:

So, let’s take that out and we are back to old, classical vector algebra. It’s like a person with a bunch of sticks to figure out which one to lay where in a 2-D plane to separate one class of objects from another, provided class definitions are already known. 

The problem is which particular shape and length must be chosen to show maximum contrast between classes.

We need to arrive at a function definition, in such a way that the value a given function takes changes drastically (e.g. from a large positive value to a large negative value).

SVM is often great for two-class classification problems, and different variants also work well for multi-class problems.

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Fraud Detection with Flink

Alexander Fedulov gives us a case study of using Apache Flink for fraud detection:

In this blog post, we have discussed the motivation behind supporting dynamic, runtime changes to a Flink application by looking at a sample use case – a Fraud Detection engine. We have described the overall architecture and interactions between its components as well as provided references for building and running a demo Fraud Detection application in a dockerized setup. We then showed the details of implementing a dynamic data partitioning pattern as the first underlying building block to enable flexible runtime configurations.

To remain focused on describing the core mechanics of the pattern, we kept the complexity of the DSL and the underlying rules engine to a minimum. Going forward, it is easy to imagine adding extensions such as allowing more sophisticated rule definitions, including filtering of certain events, logical rules chaining, and other more advanced functionality.

It was an interesting discussion and you can grab the code as well.

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Combining SAS + R for ML

Sophia Rowland has a demo of working with SAS Cloud Analytic Service from R:

The Scripting Wrapper for Analytics Transfer, also known as SWAT, is a package that allows R users to access the power of the SAS Cloud Analytic Service (CAS) from a familiar R interface. The SWAT package is available to SAS Visual Analytics (VA), SAS Visual Statistics (VS), and SAS Visual Data Mining and Machine Learning (VDMML) users. To begin working with SWAT, download and install the package from the SAS Software GitHub page.

Read on for the demo.

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Time Series Anomaly Detection with Power BI

Leila Etaati takes us through time series anomaly detection with Cognitive Services and Power Query:

I am excited about this blog post, this is based on the New service in Cognitive Service name “Anomaly Detection” which is now in Preview.
I recorded a video about how it works in cognitive service https://youtu.be/7ZOtZDbn6gM. 

However, I am going to talk about how to use it in Power BI. In this post first, a brief introduction to the anomaly detection will be presented, then how it can be used inside Power BI will be discussed.

It sounds like there are still some rough edges, but they already have the makings of an interesting service.

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