Leveraging Unlabeled Image Data With Self-Supervised Learning or Pseudo Labeling With Mateusz Opala - neptune.ai

This article was originally an episode of MLOps Live, an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Mateusz Opala about leveraging unlabeled image data with self-supervised learning or pseudo-labeling. But, if you prefer a written version, here it is! Sabine: With us today, we have Mateusz Opala, who is going to be answering questions about leveraging unlabeled image data with self-supervised learning or pseudo-labeling. Sabine: It's great to have you. Mateusz has held a number of leading machine learning positions at companies like Netguru and Brainly. So, Mateusz, you have a background in computer science, but how did you get more into the machine learning side of things? Mateusz: It all started during my sophomore year at university. One of my professors told me that Andrew Ng was doing his first iteration of the famous course on machine learning on Coursera. I kind of started from there, then did a bachelor thesis on deep unsupervised learning and went to Siemens to work in deep learning, and then all my positions were strictly about machine learning. Sabine: You've been on that path ever since? I worked for some time before as a backend engineer. But for most of the time in my career, I was a machine learning engineer/data scientist. Sabine: Mateusz, to warm you up.