Now that you’ve built your model, now what?

The next step is deployment and, arguably, it’s the most important.

That final stage – the crucial cog in your machine learning or deep learning project – is model deployment. You need to be able to get the model to the end user, right? And here’s the irony – the majority of courses, influencers, and even experts – nobody espouses the value of model deployment

 

As developers, we have the ability to use machine learning to our applications to create very unique experiences and solve difficult problems for our customers.

In this video, Martina and Bruno Capuano sit with Scott to show us how we can use Custom Vision and IoT devices to recognize objects in the real world.

This is a great demo of how to get started with ML without having to train a model, and also how to get our children interested in technology.

Related Links

Lex Fridman interviews Alex Garland, writer and director of many imaginative and philosophical films from the dreamlike exploration of human self-destruction in the movie Annihilation to the deep questions of consciousness and intelligence raised in the movie Ex Machina.

OUTLINE:
0:00 – Introduction
3:42 – Are we living in a dream?
7:15 – Aliens
12:34 – Science fiction: imagination becoming reality
17:29 – Artificial intelligence
22:40 – The new “Devs” series and the veneer of virtue in Silicon Valley
31:50 – Ex Machina and 2001: A Space Odyssey
44:58 – Lone genius
49:34 – Drawing inpiration from Elon Musk
51:24 – Space travel
54:03 – Free will
57:35 – Devs and the poetry of science
1:06:38 – What will you be remembered for?

This is the final, part 4 of a four-part series that breaks up a talk that Seth Juarez gave at the Toronto AI Meetup.

Part 1, Part 2 and Part 3 were all about the foundations of machine learning, optimization, models, and even machine learning in the cloud.

In this video Seth shows an actual machine learning problem (see the GitHub repo for the code) that does the important job of distinguishing between tacos and burritos.

The primary concepts included is MLOps both on the machine learning side as well as the deliver side in Azure Machine Learning and Azure DevOps respectively.