Sebastian Rhode
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.
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