ML.NET allows .NET developers to easily build and also consume machine learning models in their NET applications.

In this episode, Bri Achtman joins Rich to show off some really interesting scenarios that ML.NET and its family of tools enables. They talk about training models, AutoML, the ML.NET CLI, and even a Visual Studio Extension for training models!

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Without good models and the right tools to interpret them, data scientists risk making decisions based on hidden biases, spurious correlations, and false generalizations.

This has led to a rallying cry for model interpretability.

Yet the concept of interpretability remains nebulous, such that researchers and tool designers lack actionable guidelines for how to incorporate interpretability into models and accompanying tools.

This panel discussion hosted by Microsoft Research brings together experts on visualization, machine learning and human interaction to present their views as well as discuss these complicated issues.

Did you know that you can now train machine learning models with Azure ML once and deploy them in the Cloud (AKS/ACI) and on the edge (Azure IoT Edge) seamlessly thanks to ONNX Runtime inference engine.

In this new episode of the IoT Show, learn about the ONNX Runtime, the Microsoft built inference engine for ONNX models – its cross platform, cross training frameworks and op-par or better performance than existing inference engines.
From the description:
We will show how to train and containerize a machine learning model using Azure Machine Learning then deploy the trained model to a container service in the cloud and to an Azure IoT Edge device with IoT Edge across different HW platform – Intel, NVIDIA and Qualcomm.

Have you wondered whether there could be an ultimate solution to speed up your data science work via parallelizing Pandas and NumPy?

Can you boost the speed by integrating all of these data frames with libraries like XGBoost or Sklearn?

Well, then Dask may be just what you’ve been wanting all along.

Dask is a revolutionary tool, and a perfect solution if use Pandas and Numpy and struggle with the data that does not fit into RAM In this article, we will be looking at how easily the dask data frames fits into the data science workflow

In this video, Siraj Raval explores Automatic Machine Learning or “AutoML,” a field of Artificial Intelligence that’s gaining a lot of ground of late. The idea is that doing any kind of task related to machine learning involves a whole lot of steps like cleaning a dataset, choosing a model, deciding what the right configurations of that model should be, deciding what the most relevant features are etc.

From the video description:

The goal of AutoML is to automate all of that up to a point where all a data scientist would need to do is tell a machine to perform some task using a dataset and wait for it to learn how by itself. In this episode, i’m going to explain several popular AutoML techniques, then compare top AutoML frameworks like AutoKeras, Auto Sklearn, h20, Ludwig, etc. to help you decide which one will be the best for your needs.

ML.NET, Microsoft’s open source machine learning framework, has been updated to version 1.2. Here’s a quick rundown of the updates. Read the article on Visual Studio Magazine to find out more.

  • General availability of TimeSeries support for forecasting and anomaly detection:
  • General availability of ML.NET packages to use TensorFlow and ONNX models:
  • Easily integrate ML.NET models in web or serverless apps with Microsoft.Extensions.ML integration package (preview):
  • ML.NET CLI updated to 0.14 (preview):
  • Model Builder updates:
    • Expanding support to .txt files and more delimiters for values

    • No limits on training data size

    • Smart defaults for training time for large datasets

    • Improved model consumption experience