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World Congress 2021 • Jun 28, 2021

Industrializing your Data Science capabilities

Dubravko Dolic , Hüdaverdi Cakir

When pinpointing defective tires, Continental realized localized models couldn't scale. Discover how they built a Kubernetes-backed Data Science Factory for automated, industrial-grade machine learning deployments.

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#1 about 3 min

Resolving urgent tire quality issues with rapid automation

Running a local pipeline inside a robust infrastructure resolves a critical safety recall in under a day.

#2 about 3 min

Evolving infrastructure from standalone scripts to industrialized pipelines

Moving from local models for demand forecasting into a structured factory environment enables repeatable scaling.

#3 about 4 min

Implementing continuous delivery architecture for machine learning

Transitioning past basic agile concepts allows teams to use continuous delivery for rapid artificial intelligence iteration.

#4 about 3 min

Abstracting deployment complexity for non-engineering data scientists

Teaching git and deployment stages eliminates operational hurdles for mathematicians and psychologists transitioning to programming.

#5 about 5 min

Ensuring predictable release cycles through multi-stage container deployments

Managing immutable container images through development, testing, and production ensures isolated and reliable workload execution.

#6 about 3 min

Inspecting a real-time fleet management telemetry dashboard

Streaming vehicle metrics into an interactive interface highlights practical implementation details of production analytical pipelines.

#7 about 4 min

Managing container stage promotions using custom command line tooling

Applying dedicated command line utilities simplifies transferring built images across operational namespaces securely.

#8 about 3 min

Defining docker packages and kubernetes job manifests

Binding source code artifacts to cluster-specific configuration manifests dictates precise runtime provisioning without manual intervention.

#9 about 3 min

Verifying live cluster upgrades and triggering configuration rollbacks

Evaluating active web connections confirms successful workload replacements and demonstrates swift recovery capabilities during failures.

#10 about 4 min

Monitoring platform health streams with integrated logging observability

Instrumenting applications with distributed logs provides data scientists the autonomy to diagnose memory and execution anomalies.

#11 about 2 min

Obscuring cluster complexities through declarative platform deployment templates

Offering simplified descriptors removes deep infrastructure orchestration requirements without sacrificing underlying container configuration flexibility.

#12 about 3 min

Provisioning preconfigured experimental lab environments for data scientists

Supplying standardized interactive workspaces guarantees consistent access to modern deep learning frameworks without initial configuration hurdles.

#13 about 3 min

Extending predictive modeling to massive mining equipment maintenance

Integrating onboard logger telemetry with interactive reporting empowers physicists to optimize operational longevity securely.

#14 about 3 min

Deploying anomaly detection models directly into manufacturing hardware

Executing remote diagnostic scoring onto localized edge hardware captures rare manufacturing defects immediately.

#15 about 4 min

Centralizing telemetry aggregation pipelines and computer vision labeling

Combining automated imagery categorization and vehicle metrics feeds broader predictive maintenance alerting loops successfully.

#16 about 3 min

Discussing open-source possibilities and identifying viable team sizes

Determining appropriate organizational scales dictates when to invest in continuous delivery architectures and custom tooling platforms.

Matching moments

3:31 min

Practical learnings from deploying containerized AI solutions

Sebastian Rhode Sebastian Rhode · WWC 2024

2:04 min

Optimizing and deploying containerized AI inference workloads

Ankit Patel Ankit Patel · WWC 2024

2:03 min

Solving complex engineering challenges in artificial intelligence deployment

Nico Axtmann · WWC 2022

2:37 min

Shifting to containerized AI deployment environments

Sebastian Rhode Sebastian Rhode · WWC 2024

11:07 min

Designing an automated and orchestrated machine learning target process

Simon Stiebellehner · WWC 2021

1:09 min

Deploying algorithms and AI models to edge production

Irina Terekhova Irina Terekhova · WWC Europe 2026

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