El poder de la observabilidad en Machine Learning
¿Cuántas veces has realizado un proyecto de Machine Learning completo y una vez finalizado el comportamiento no es el deseado? ¿Y cuántas de esas veces no somos capaces de encontrar el origen de dichos problemas para solucionarlos?
La observabilidad en Machine Learning es la capacidad de obtener información sobre el rendimiento de nuestro modelo durante todos los pasos de Machine Learning. Cuando trabajamos en Machine Learning, tenemos claros los pasos a seguir, como la lógica de negocio, análisis de datos, entrenamiento y despliegue, todo ello por supuesto bajo las prácticas de MLOps. Incluir observabilidad en nuestros proyectos de Machine Learning nos permitirá detectar errores, encontrar su origen y subsanarlos lo antes posible para su mejora continua.
En esta charla hablaremos sobre qué es la observabilidad en Machine Learning y por qué es importante en nuestros proyectos. Veremos cómo podemos trabajar para obtener un sistema observable y aprenderemos como aplicar dichas técnicas en los distintos pasos de un proceso de Machine Learning.
Sara San Luis
Machine Learning Engineer at Plain Concepts
I love learning new things and I’ve been told that I pass this love on to the rest of the team. I strongly believe that there’s no better place to learn that outside my comfort zone, that’s why I’m always seeking for new opportunities to challenge myself both professionally and personally. And as important as learning is teaching what you learn to others – “knowledge is power, knowledge shared is power multiplied”, that’s the reason why I enjoy so much taking part in technical events as a speaker.
Currently working in the latest Artificial Intelligence (Machine Learning and Deep Learning) and Data applications integrating them into working practices and side projects, with a preference for AI for Good projects, as they allow me to use my passion to help people out!
Christian Carballo
Machine Learning Engineer at Plain Concepts
Machine Learning Engineer at Plain Concepts.
He holds a B.S. in Mathematics and M.S. in Applied Mathematics from the University of the Basque Country.
He has been working in diverse environments, such as in Research, Research & Development, and Software Engineering, all of them pivoting around Machine Learning, Algorithms, and Optimization.
As Data Scientist, he deploys end-to-end cloud-based AI solutions.
As Mathematician, he enjoys deepening in the algorithms for understanding and developing them.