Adam Alpire
About Adam Alpire
Adam Alpire is a Staff MLOps Engineer at CAPE Analytics, specializing in the deployment and serving of machine learning models. He holds a Bachelor's degree in Computer Science and Engineering and a Master's degree in Data Mining and Distributed Systems, with extensive experience in MLOps and data science roles across various companies.
Work at Cape Analytics
Adam Alpire serves as a Staff MLOps Engineer at Cape Analytics since 2022. In this role, he supports the deployment and serving of over 150 machine learning models using Triton Server for neural networks and BentoML for traditional models. His work facilitates both batch and real-time computing, contributing to the efficiency of the company's analytics capabilities. He collaborates with strategic teams, including Business Intelligence and Data Partnerships, to provide insights that enhance data contracts and understanding of usage patterns.
Education and Expertise
Adam Alpire holds a Bachelor's degree in Computer Science and Engineering from Universidad Politécnica de Madrid, completed from 2010 to 2014. He furthered his education with a Master's Programme in ICT Innovation: Data Science at EIT Digital Master School from 2015 to 2016. Additionally, he earned a Master's Degree in Data Mining and Distributed Systems for Really Big Data from KTH Royal Institute of Technology in 2016 to 2017. His academic background provides a strong foundation for his expertise in machine learning operations.
Background
Adam Alpire has a diverse professional background in data science and machine learning operations. He began his career as a Software Developer at Fibernet, S. L. in 2013. He then transitioned to roles as a Data Science Intern at TELEFÓNICA OPEN FUTURE_ and RISE SICS. Adam gained significant experience at Siemens, where he worked as a Data Scientist, Senior Data/ML Engineer, and Lead MLOps Engineer, contributing to various projects in Erlangen, Germany.
Achievements
Throughout his career, Adam Alpire has led the deployment and serving of over 100 different models in production, achieving millions of predictions per day on both AWS and GCP. His contributions have been instrumental in developing the company's Data Governance strategy and supporting the AI Governance strategy. His role in serving machine learning models has enhanced the operational capabilities of the organizations he has worked with.