Spotify has used Machine Learning solutions in its products since the early days. Recommending content is the most obvious place where Machine Learning solutions flourished at Spotify early on. We had seen the benefits of ML powered solutions and had a large appetite for delivering more value to our customers. But we also saw the costs of democratizing Machine Learning solutions across the organization. We knew that Machine Learning solutions require a conscious, informed decision by all stakeholders of a product.
So how did Spotify promote exploration and conscious use of Machine Learning?
We will share 3 lessons learnt a year of running a Machine Learning training for all members of RnD from product to research, to engineering and design.
4. Industry context
● Data costs
● System costs
● Talent costs
● Customer-facing cost
Photo by Nina Uhlíková from Pexels
5. Spotify context
● Spotify has a strong tech learning culture
In 2020, 227 Spotifiers volunteered to teach and facilitate 130+ courses and onboarding programs
● Tech Learning is an L&D infra team
We focus on Spotify specific technical content. Enrollment is voluntary and self-motivated.
● Cross-functional teams demand democratization of ML knowledge
ML solutions are powerful when used for the right problems.
10. 99.5% of the participants have a good
mental model and basic intuition about
Machine Learning at Spotify.
Reviews of the learning
experience are consistent at
4.5 / 5
Outcomes
11. 99.5% of the participants have a good
mental model and basic intuition about
Machine Learning at Spotify.
Reviews of the learning
experience are consistent at
4.5 / 5
Outcomes
Within 1 day I was already using the
training in my every day work, and
feeling more confident having
conversations about it. It even
affected my roadmap plans in a really
positive way. The practical
application of this course was the
best part for me!
12. 99.5% of the participants have a good
mental model and basic intuition about
Machine Learning at Spotify.
Reviews of the learning
experience are consistent at
4.5 / 5
Outcomes
I enjoyed the discussions with various
people in different domains about ML
solutions. I liked that the course
offered a good focus on deterministic
factors on whether ML is the
appropriate solution for a use case or
not.
13. 99.5% of the participants have a good
mental model and basic intuition about
Machine Learning at Spotify.
Reviews of the learning
experience are consistent at
4.5 / 5
Outcomes
I started this course thinking that bold
ML futures would mean less need for
disciplines like User Research, but
something that stood out today - if
anything, there is more need.
Courses like this provide language
and places for focus.
15. Lesson 1: Educate
and empower all
business stakeholders
Photo
by
picjumbo.com
from
Pexels
16. Educate & empower all business stakeholders
● Create sessions around the needs of each type of stakeholder.
● Incorporate the context of your learners into the training.
● Build tech empathy
17. Lesson 2: Design and
develop education
programs as products
Photo
by
Andrea
Piacquadio
from
Pexels
18. Design & develop education programs as products
● Structure of the course
● Content of individual sessions
● Focus on opportunities to practice
Content
Creation
Training
Delivery
Feedback
Analysis
19. Lesson 3: Tap into the
pool of experience as
diverse as your learners
Photo
by
Wendy
Wei
from
Pexels
20. Tap into the diverse pool of experience
● Course team teaching the course includes a wide range of disciplines
● Blur the lines between the experts and learners
