Maya Bogdanova is a Senior Education Program Manager at Tech Learning team, Spotify. Maya is instructional designer by trade with 7+ years of experience in designing and developing learning experiences.
At Spotify she has been leading internal educational efforts that aim to grow technical skills of all RnD members with focus on Machine Learning Engineering, Backend Engineering and Engineering Practices as a whole.
Maya is particularly interested in narrowing the gap between the context of work, learning experience and application of new skills. In her daily work she manages multiple teams of subject matter experts from Spotify to create and deliver impactful learning opportunities. At heart, she just loves to learn and create, and help others do the same.
Everything is labelled as AI these days. We want to get past the marketing and dig into the details of when teams should investigate AI solutions, the pitfalls that they need to look out for and the best practices for ensuring successful outcomes.
Moderated by Adam Bermingham, this panel will discuss when and where to best leverage machine learning for maximum product team productivity.
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.