Automating Machine Learning Operations on AWS - Seguridad Mania.com - España y América Latina

Machine Learning (ML) has revolutionized how we’ve solved business problems over the last decade. The ability to collect and store limitless data, coupled with advancements in computing and networking, has led to the use of Machine Learning in several business verticals. However, developing end to end machine-learning pipelines and workflows that provide continuous and adaptive business insights to other applications or users is a daunting task. This is primarily because of an inherent gap in how data scientists develop the machine learning models and how ML operations teams promote and deploy them into the production environments. Onica’s Model Factory aims to bridge this gap by providing a seamless development, tracking and deployment experience for data scientists and operators alike. This ML Framework enables support for numerous machine learning models in production and development simultaneously along with CI/CD for data science and automated workflows for Development, QA, and Production. What we'll cover: - State of the art: Gap between the Data Scientists and ML Operations teams - What is a Model Factory framework? - How does Model Framework Integrate with AWS services? - Model Factory Demo that will explore: - Quick feedback and traceability for model development - ML framework agnostic tooling for packaging of models - Platform agnostic continuous/rolling deployment