Shubham Krishna
About Shubham Krishna
Shubham Krishna is a Machine Learning Engineer with extensive experience in developing scalable machine learning pipelines and deploying advanced models. He has worked with notable organizations such as Samsung Electronics and Bosch Center for Artificial Intelligence, and has contributed to open-source projects in the field.
Work at ML6
Shubham Krishna has been employed at ML6 as a Machine Learning Engineer since 2021. In this role, he focuses on developing and implementing machine learning solutions. His work involves creating scalable systems that enhance the efficiency and effectiveness of machine learning applications. Shubham's contributions to ML6 include leveraging advanced technologies to solve complex problems in various domains.
Education and Expertise
Shubham Krishna holds an Integrated Master of Technology in Mathematics and Computer Science from the Indian Institute of Technology (Indian School of Mines), Dhanbad, where he studied from 2013 to 2018. He furthered his education by obtaining a Master of Science in Machine Learning from the University of Tübingen, completing his studies from 2019 to 2021. His educational background equips him with a strong foundation in both theoretical and practical aspects of machine learning.
Background
Shubham Krishna has a diverse professional background in machine learning and artificial intelligence. He began his career as a Machine Learning Research Intern at Samsung Electronics in 2017. He later worked as an Applied Research Engineer at Samsung Electronics from 2018 to 2019. Additionally, he served as a Master Thesis Student at Bosch Center for Artificial Intelligence in 2021 and as a Research Assistant at the Max Planck Institute for Intelligent Systems from 2020 to 2021.
Achievements
Shubham Krishna has made significant contributions to the field of machine learning. He developed scalable pipelines using the Apache Beam RunInference API, which were featured on the Apache Beam website. He successfully deployed various Stable Diffusion models on AWS EC2, generating over 3 million images in the first month. He also trained and deployed product recommendation models using Google's Retail API, achieving a 40% increase in conversion rates for a retail company's e-commerce platform. Additionally, he open-sourced a custom-named entity recognition model on Hugging Face, which has been downloaded over 300,000 times.