Every organization is leveraging machine learning (ML) to provide increasing value to their customers and understand their business. You may have created models too. But, how do you scale this process now? In this case study, you will learn how to pinpoint inefficiencies in your ML data flow, how SurveyMonkey tackled this, and how to make your data more usable to accelerate ML model development.
In this episode
Shubhi (Shubhankar) Jain
Machine Learning Engineer, SurveyMonkey
Shubhi Jain is a machine learning engineer at SurveyMonkey where he develops and implements machine learning systems for its products and teams. Occasionally, he’ll create YouTube videos about Machine Learning in collaboration with Springboard, an e-learning platform. He’s always excited to bring his expertise and passion for Data and AI systems to the rest of the industry. In his free time, Shubhi likes hiking with his dog and accelerating his hearing loss at live music shows.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.