Containerized architectures for deep learning
Who is this presentation for?
- Data engineers and machine learning engineers
Container and cloud native technologies around Kubernetes have become the de facto standard in modern ML and AI application development. And while many data scientists and engineers tend to focus on tools, the platform that enables these tools is equally important and often overlooked.
Antje Barth examines some common architecture blueprints and popular technologies used to integrate AI into existing infrastructures and explains how you can build a production-ready containerized platform for deep learning. In particular, she explores Docker and Kubernetes, with its associated cloud native technologies, and its use and advantages in ML/AI environments.
You’ll see a demo of a microservices-based AI application and walk through a typical ML workflow—data ingestion, preprocessing, continuous model training, evaluation, model deployment, and serving—to see the technologies in action.
- A basic understanding of machine learning
- General knowledge of container technologies and Kubernetes (useful but not required)
What you'll learn
- Learn how to build a flexible and elastic containerized platform and leverage streaming data architectures and newly emerged pipeline tools like Kubeflow to manage data logistics and enable production-ready ML and AI workflows
Antje Barth is a senior developer advocate for AI and machine learning at AWS. Besides AI and ML, Antje is passionate about helping developers leverage big data, container, and Kubernetes platforms in the context of AI and machine learning. Previously, Antje was in technical evangelist and solutions engineering roles at MapR and Cisco. She frequently speaks at AI and machine learning conferences and meetups around the world. Antje is a cofounder of the Düsseldorf chapter of Women in Big Data.
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