Sebastian Rhode

Using Containers to deploy AI Models across our microscopy platform

How do you guarantee identical AI results from the cloud to a local desktop? Learn how containers solved this critical reproducibility challenge for scientific imaging.

Using Containers to deploy AI Models across our microscopy platform
#1about 4 minutes

AI-powered computer vision workflows in modern microscopy

Zeiss uses AI for various microscopy tasks like classification and instance segmentation to analyze biological samples.

#2about 2 minutes

The challenge of analyzing terabyte-scale microscopy data

Automated microscopy workflows can generate terabytes of data from a single experiment, requiring powerful AI for quantitative analysis like cell counting.

#3about 3 minutes

Key requirements for reproducible AI model deployment

Users need robust and reproducible AI models that deliver consistent results across different platforms without requiring IT expertise.

#4about 3 minutes

Moving from model artifacts to containerized deployments

The previous method of deploying only model files created synchronization issues, leading to the adoption of containers to package models with all their dependencies.

#5about 3 minutes

Why containers are the ideal solution for AI deployment

Containers solve key challenges by enabling GPU access on Windows via WSL2, decoupling dependencies for different AI tasks, and simplifying client software maintenance.

#6about 3 minutes

The new workflow for training and deploying models

The new process involves training models in the cloud, which produces a container as the final artifact that is then downloaded and run by the client software.

#7about 2 minutes

Demonstrating the business value of containerization

This container-based approach allows users to access new AI algorithms faster without client updates, convincing stakeholders and enabling independent development cycles.

#8about 4 minutes

Key learnings for adopting container technology

Adopting containers is successful when it solves a real business problem, starts with smaller prototype projects to de-risk, and leverages mature, standardized technology.

Related jobs
Jobs that call for the skills explored in this talk.

Featured Partners

Related Videos

Empowering Thousands of Developers: Our Journey to an Internal Developer Platform29:26

Empowering Thousands of Developers: Our Journey to an Internal Developer Platform

Bastian Heilemann, Bruno Margula

Compose the Future: Building Agentic Applications, Made Simple with Docker50:09

Compose the Future: Building Agentic Applications, Made Simple with Docker

Mark Cavage, Tushar Jain, Jim Clark, Yunong Xiao

Supercharge your cloud-native applications with Generative AI32:13

Supercharge your cloud-native applications with Generative AI

Cedric Clyburn

Containers and Kubernetes made easy: Deep dive into Podman Desktop and new AI capabilities32:19

Containers and Kubernetes made easy: Deep dive into Podman Desktop and new AI capabilities

Stevan Le Meur

ZEISS & Microsoft - Building the Next Generation Medical Ecosystem in the Cloud20:32

ZEISS & Microsoft - Building the Next Generation Medical Ecosystem in the Cloud

Leo Lindhorst

Bootable AI Containers with Podman Desktop29:19

Bootable AI Containers with Podman Desktop

Kevin Dubois, Cedric Clyburn

Bringing AI Everywhere26:25

Bringing AI Everywhere

Stephan Gillich

Industrializing your Data Science capabilities43:47

Industrializing your Data Science capabilities

Dubravko Dolic & Hüdaverdi Cakir

From learning to earning

Jobs that call for the skills explored in this talk.