As machine learning (ML) becomes more and more of a regular component in software companies this day and age, development teams also start requiring to handle the complexity of deploying ML models into production.
In this talk, we talk about Machine Learning Operations (MLOps), a set of best practices that aims to improve the reliability and efficiency of production rising ML pipelines from the early steps of data extraction until the final step of providing the model to the users.
👉 How does ML deployment differ from classic software deployment?
👉 What is MLOps?
👉 An example of how to apply MLOps in your company
🧑💻 Sho Virtucio
Sho is a software engineer going way back since 2014 jumping from classic jQuery web development to iOS/Android, to React and UI/UI design, to back-end systems with Node and Go, and finally infrastructure for ML with Kubernetes. He has worked in both large and small companies in his career and started his MLOps journey almost accidentally after being lucky enough to be a solo engineer working with a DeepMind researcher to build a deep learning solution’s end-to-end infra/backend/frontend system on generating product labels for a Japanese drink company.
He now works at a retail company production rising machine learning infrastructure for recommendation systems for smart shopping carts in Japan and also writes and directs a manga/anime series about tech startups in Japan in his time outside of work.
*ABOUT CODE CHRYSALIS*
Code Chrysalis 🦋
➡️ is a Tokyo-based 🗼 coding school providing full-time and part-time courses in English and Japanese.
Join us 🤗 in person or take our classes remotely 💻 . See why we are an industry leader in technical education in Japan 🇯🇵.