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Category: Machine Learning

Environments in Azure ML

Published 2021-09-14 by Kevin Feasel

Luis Valencia explains what environments are in Azure ML:

An Environment defines Python packages, environment variables, and Docker settings that are used in machine learning experiments, including in data preparation, training, and deployment to a web service. An Environment is managed and versioned in an Azure Machine Learning Workspace. You can update an existing environment and retrieve a version to reuse. Environments are exclusive to the workspace they are created in and can’t be used across different workspaces.

In basic terms for a developer, it’s basically a Docker Image with all the needed dependencies (conda/pip packages) to run your experiment.

A friendly word of advice from some bad experiences: stick with the curated environments as much as you can. Those are easy and rarely fail. Building your own environments from Conda files is a possibility, but it’s an, err, probabilistic exercise as to whether your compute target will actually work or not.

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Image Classification with ML.NET

Published 2021-09-07 by Kevin Feasel

Ivan Matec shows how to use ML.NET’s image classification with an example of vital importance:

One of the best scenes from Silicon Valley is Jian Yang demoing his “Hotdog, not hotdog” application. In this article, we will build our own “Hotdog, not hotdog” solution using ML.NET. After all, who would not want to determine if that dish is, or is not a hot dog? Just take a picture, upload it to the web or desktop application, and get results with almost 90% certainty in a second.

Although some may say this is not a very useful application, it is a fun way to explore another machine learning concept through ML.NET. I covered installing and getting started with ML.NET in Visual Studio in my previous article, so refer to it if you missed it.

Click through for the implementation, which is quite straightforward.

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Databricks Autologging

Published 2021-08-31 by Kevin Feasel

Corey Zumar and Kasey Uhlenhuth announce a new product:

Machine learning teams require the ability to reproduce and explain their results–whether for regulatory, debugging or other purposes. This means every production model must have a record of its lineage and performance characteristics. While some ML practitioners diligently version their source code, hyperparameters and performance metrics, others find it cumbersome or distracting from their rapid prototyping. As a result, data teams encounter three primary challenges when recording this information: (1) standardizing machine learning artifacts tracked across ML teams, (2) ensuring reproducibility and auditability across a diverse set of ML problems and (3) maintaining readable code across many logging calls.

Read on to see how Databricks Autologging can satisfy these issues.

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From API Call to ML Services Prediction

Published 2021-08-20 by Kevin Feasel

Tomaz Kastrun continues a series:

From the previous two blog posts:

– Creating REST API for reading data from Microsoft SQL Server in web browser

– Writing Data to Microsoft SQL Server from web browser using REST API and node.js

We have looked into the installation process of Node.js, setup of Microsoft SQL Server and made couple of examples on reading the data from database through REST API and how to insert data back to database.

In this post, we will be looking the R predictions using API calls against a sample dataset.

Click through to see it in action.

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Shrinking Convolutional Neural Networks for TinyML

Published 2021-08-06 by Kevin Feasel

Pete Warden writes up a tip:

A colleague recently asked for more details on an approach I recommended, but which she hadn’t seen any documentation for. I realized that it was something I’d learned from talking to model builders at Google, and I wasn’t sure there was anything written up, so in the spirit of leaving a trail of breadcrumbs for anyone coming after, I thought I should put it into a quick blog post.

The summary is that if you have MaxPool or AveragePool after a convolutional layer in a network, and you’re targeting a resource-constrained system like a microcontroller, you should try removing them entirely and replacing them with a stride in the convolution instead. This has two main benefits, but to explain it’s easiest to diagram out the network before and after.

Click through for the full explanation.

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Deploying Custom Docker Images in Azure ML

Published 2021-08-05 by Kevin Feasel

Tsuyoshi Matsuzaki shows us how to deploy an Azure ML model via custom Docker image:

In my early post, I have showed you how to bring your own custom docker image in training with Azure Machine Learning.
On the contrary, here I’ll show you how to bring custom docker image in model deployment.

In Azure Machine Learning, the base docker image in deployment includes the inferencing assets, such as, Flask server, etc. So you should use AML compliant image for base image, even when you use your own custom docker image.
The list of these maintained AML images is available in https://github.com/Azure/AzureML-Containers .

Read on for an example.

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An Introduction to ML.NET

Published 2021-07-30 by Kevin Feasel

Ivan Matec gives us a walkthrough of the ML.NET library and its Model Builder component:

Before we dive into our example, let’s talk a bit about ML.NET history and its current state.

ML.NET draws its origins from the 2002’s Microsoft Research project named TMSN, which stands for “test mining search and navigation.” Later it was renamed to TLC, “the learning code.” ML.NET war derived from the TLC library. Initially, it was used on internal Microsoft products.

The first publicly available version ML.NET 1.0 was released in 2019. It included the Model Builder add-in and AutoML (Automated Machine Learning) capabilities.

The current version is 1.6.0. More details about all releases can be found on the official ML.NET release page.

ML.NET is not a bad library if you need to do some fairly simple work

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Explaining an ML Model with SHAP

Published 2021-07-29 by Kevin Feasel

Dan Lantos, et al, walk us through one technique for model explainability:

Interpretability has to do with how accurately a machine learning model can associate a cause (input) to an effect (output). 

Explainability on the other hand is the extent to which the internal mechanics of a machine or deep learning system can be explained in human terms. Or to put it simply, explainability is the ability to explain what is happening. 

Let’s consider a simple example illustrated below where the goal of the machine learning model is to classify an animal into its respective groups. We use an image of a butterfly as input into the machine learning model. The model would classify the butterfly as either an insect, mammal, fish, reptile or bird. Typically, most complex machine learning models would provide a classification without explaining how the features contributed to the result. However, using tools that help with explainability, we can overcome this limitation. We can then understand what particular features of the butterfly contributed to it being classified as an insect. Since the butterfly has six legs, it is thus classified as an insect.

Being able to provide a rationale behind a model’s prediction would give the users (and the developers) confidence about the validity of the model’s decision.

Read on to see how you can use a library called SHAP in Python to help with this explainability.

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Explaining Chatbots

Published 2021-07-28 by Kevin Feasel

Will Harris takes us through the basics of chatbots:

You can develop your business processes into conversational workflows to help people perform tasks. This can be wide ranging; from looking up records to do with their accounts, through to engaging in new services. There are many processes that can be turned into an effective conversational workflow. This typically helps people perform activities more inclusively and conveniently or helps reduce grunt work for employees.

Brisa2, an automotive company, developed a bot to help find company data and perform tasks like password resets, helping to free up the IT team for other tasks.

Click through for an overview of the concept. I’ve been down on this generation of chatbot because, as a user, they usually show up in places where a well-designed UI would be faster and more effective—as well as less prone to failures in understanding.

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Reinforcement Learning and Python 3

Published 2021-07-21 by Kevin Feasel

I have a new post up:

I finally got around to trying out a reinforcement learning exercise this weekend in an attempt to learn about the technique. One of the most interesting blog posts I read is Andrej Karpathy’s post on using reinforcement learning to play Pong on the Atari 2600. In it, Andrej uses the Gym package in Python to play the game.

This won’t be a post diving into the details of how reinforcement learning works; Andrej does that far better than I possibly could, so read the post. Instead, the purpose of this post is to provide a minor update to Andrej’s code to switch it from Python 2 to Python 3. In doing this, I went with the most convenient answer over a potentially better solution (e.g., switching xrange() to range() rather then re-working the code), but it does work. I also bumped up the learning rate a little bit to pick up the pace a bit.

Click through for the (slightly) updated code.

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