World Congress 2024 • Aug 20, 2024 • Session details

Using Containers to deploy AI Models across our microscopy platform

Sebastian Rhode

Tired of dependency hell when shipping AI models to client environments? Discover how containerizing workloads with Docker and WSL2 unlocks native GPU access and decouples your stack.

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#1 about 2 min

Introduction to Zeiss and microscopy technology

An overview of the Zeiss business units focused on industrial quality and research microscopy.

#2 about 2 min

Exploring common AI workflows in microscopy tasks

How computer vision handles classification and instance segmentation of biological samples.

#3 about 4 min

Managing large data sets in microscopy workflows

The transition from gigabyte to terabyte datasets when capturing time-lapse imaging of growing cells.

#4 about 4 min

Customer requirements for AI model delivery

The need for robust AI performance across cloud and local platforms without requiring IT expertise.

#5 about 2 min

Challenges in traditional AI deployment methods

Why decoupling the model from client software caused persistent and difficult dependency mismatches.

#6 about 3 min

Shifting to containerized AI deployment environments

Bundling models and all necessary dependencies into Linux containers ensures consistent execution across environments.

#7 about 5 min

The containerized model training and deployment pipeline

Generating a containerized artifact after cloud training drastically simplifies distribution to image analysis clients.

#8 about 2 min

Executing containerized AI in production workflows

Downloading and running the self-contained AI module locally integrates smoothly into the user perspective.

#9 about 2 min

Business value of independent AI module updates

Delivering improved AI features quickly via containers speeds up progress without risking client software stability.

#10 about 4 min

Practical learnings from deploying containerized AI solutions

Starting with prototypes and open container standards minimized deployment risk in a traditional engineering company.

Matching moments

2:04 min

Optimizing and deploying containerized AI inference workloads

Ankit Patel

Ankit Patel · WWC 2024

3:34 min

Standardizing AI model and tool execution using containers

Jim Clark

Jim Clark +3 · WWC 2025

2:59 min

Adopting AI tools for developer container workflows

Ali Alp

Ali Alp · WWC Europe 2026

2:40 min

Optimizing AI deployments with bootable containers

Cedric Clyburn

Cedric Clyburn +1 · WWC 2025

3:28 min

Deploying machine learning models locally and remotely with containers

Linda Mohamed · LIVE

3:34 min

Bridging application containerization and modern AI agent development

Jim Clark

Jim Clark +3 · WWC 2025

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