Microsoft Japan, Senior Cloud Solution Architect
Architect | Software Engineer | Applied Data Scientist.
I joined Microsoft in 2002. Before joining it, I join several software development project C/S, Web or so.
After Join Microsoft, Delivery operational engineering at MSN Operations. Then move to Enterprise Customer pre-sales engineer to work variety type of IT Project. Then move to Technical Evangelist to Azure developer relationship management. Then today.
Area of Expertise
Machine learning operations: Applying DevOps to data science
Many companies have adopted DevOps practices to improve their software delivery, but these same techniques are rarely applied to machine learning projects. Collaboration between developers and data scientists can be limited and deploying models to production in a consistent, trustworthy way is often a pipe dream. In this session, learn how Tailwind Traders applied DevOps practices to their machine learning projects using Azure DevOps and Azure Machine Learning Service. We show automated training, scoring, and storage of versioned models, wrap the models in Docker containers, and deploy them to Azure Container Instances or Azure Kubernetes Service. We even collect continuous feedback on model behavior so we know when to retrain.