Modern frameworks, for machine learning and for career goals
Hack.Chat with Pluralsight
“We’re excited to #hackdiversity because at Pluralsight, we want to democratize professional technology training” — Jody Bailey, Head of Technology at Pluralsight

In 2017, IBM published a report, “The Quant Crunch: How The Demand For Data Science Skills Is Disrupting The Job Market,” which highlights data particularly relevant to the value of hacking diversity:
- Demand for data scientists will soar 28% by 2020
- Jobs requiring machine learning skills are anticipated to pay an average yearly salary of $114,000.
During our Hack Chat with Pluralsight, we delved into what skills Hack Fellows might incorporate into their repertoires to ride this growth potential.
In abstract
Conor McKay and David Mashburn, machine learning engineers, introduced us to TensorFlow — a machine learning framework created by the Google Brain team that streamlines the data collection, model training, predictions, and results of traditional machine learning models.
We started the presentation with an exercise in abstract thinking: what are tensors?
We learned to think of them as arrays that can have any number of dimensions: 0, linear, 2D, 3D etc. This leads into the question of, how can we use this concept to represent and make sense of real-world data, and by extent, how can we leverage tensors to solve real-world problems?
This is where things get interesting.
In practice
We first have to train the model to learn input-target pairs i.e. tensors. For the purposes of this exercise, Conor introduced the real-world dilemma of housing costs:
- What is the relationship between housing prices and square footage? e.g. a 1:1 comparison
- What happens when there are two inputs? e.g. housing square footage and local school performance as related to housing prices
Quickly, we saw the vast variety of input-target relationships and configurations that go into training a traditional machine learning model.
Modern pathways toward success
The demand for, the incentive to gain, and the problems tackled by, machine learning skillsets are significant.
Even more impactful, was learning about the winding career journeys of the folks working in these roles at Pluralsight — a machine learning engineer by way of a political science degree, a CTO by way of a physics degree, a software architect by way of dropping out of high school in favor of hands-on education. This sample of non-traditional career paths converged on shared attributes of hustle, creativity, initiative, and tenacity to land in the roles that they’re in now.
We were energized, and inspired, to end the evening with a reflection that Hack Fellows’ journeys (and what amazing journeys of hustle, creativity, initiative, and tenacity they are) are assets, not detractions.

Special thanks and appreciation
to the Pluralsight team of Lany Watkins, Erin Barnes, and Jody Bailey for investing in the skills development, and career advancement, of Hack.Diversity talent!
Want to partner with us for an event? Let us know at info@hackdiversity.com. Engage with us on Twitter, Facebook, Instagram, LinkedIn, Website, and Support!

