Notebooks are not enough: how to deliver machine learning products without getting killed

Technology

Keywords defining the session:

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Takeaway points of the session:

- Delivering machine learning systems in the real world requires much more than designing an algorithm or training a model

- Software engineering is an essential tool for data scientists and machine learning engineers to be able to deliver machine learning systems with real world impact

Description:

Jupyter notebooks (and other notebooks-based system) are a very popular and handy tool in data science and in the design and training of machine learning models. With a notebook, you can quickly explore different alternatives, getting immediate feedback, producing plots, combining code and text, and use them as a powerful communication tool, as a collaboration tool to work with data scientists and machine learning engineers, etc.

However notebooks are not enough.

Why? Let’s talk about an story about artificial intelligence and the singularity.

Elon Musk and Mark Zuckerberg have recently engaged in a public debate about the perils of artificial intelligence (AI). On one side of the debate, Elon Musk defends that AI may give birth to future killing machines that will exploit humanity as slaves. The moment when it will happen is known as the singularity. From that moment on, machines may autonomously decide to kill us, or to enslave us, or to do us anything they please.

What Elon Musk fails to notice is that we have alredy created machines that kill humans autonomously! How did we as humankind design such a horrible machine? Small spoiler: it is actually related to software engineering.

Come to the talk and discover the relationship between software engineering, machine learning and the singularity!