05-27, 11:30–12:00 (Europe/Vilnius), PyData Room
Machine learning (ML) models rarely make it to production. Often these projects start with intention ‘let’s make something cool’, but they get stuck in local Jupyter notebooks or Power Point presentations. What does it take to complete a data-driven product?
I will talk about the importance of the problem and it's context definition, will give examples of overcomplication and together we will go through the steps required to build a data-driven product.
PyData, data science, machine learning
I started my career as a data scientist 7 years ago. Organisation I was working in was making first steps towards data science and I was one of a few ones to make those steps. I spent more than 3 years leading data science team and mentoring people who wanted to become data scientists. Currently I am working as a Head of Data at SME Bank.