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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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.

What will the future of Data Science work look like when technologies like AutoML promise to automate much of it?

Here’s an interesting look from TDWI.

AutoML is the umbrella term for tools and platforms that automate the steps of selecting the right model and optimizing its hyperparameters to generate the best model possible under a given set of data. There are libraries such as auto-sklearn and auto-WEKA that provide these autoML capabilities.

Siraj Raval has a great talk on genetic algorithms and neuroevolutionary strategies offer us a way to replicate the process of natural selection en silico.  (Bonus points for the Latin usage, Siraj!)

Google already uses self-creating AI as part of its AutoML service that finds the best model for customers.