Industrializing your Data Science capabilities

A product recall demanded a new quality script, fast. See how Continental's MLOps platform helped data scientists deploy a fix to production in under one day.

#1about 2 minutes

The challenge of industrializing data science models

A tire recall incident highlights the gap between a data scientist's local Python script and a scalable, production-ready solution.

#2about 3 minutes

Building the initial concept for a data science factory

The journey began with a demand forecasting use case, leading to the concept of a lab for experimentation and a factory for industrialization.

#3about 5 minutes

Establishing processes and a cloud-agnostic tool stack

A standardized process with dev, QA, and prod stages was created, supported by a cloud-agnostic tool stack including Git, Jenkins, and Kubernetes.

#4about 5 minutes

Technical architecture for a multi-stage deployment environment

The architecture uses Kubernetes and containerization to create reproducible dev, QA, and prod environments with immutable builds and stage-specific configurations.

#5about 11 minutes

Live demo of deploying and promoting application versions

A command-line interface is used to deploy a new version of a Shiny application to the dev environment and promote an existing build from dev to QA.

#6about 5 minutes

Monitoring applications with logs and metrics

The platform provides developers with access to Elastic Stack for log aggregation and Prometheus with Grafana for metrics to monitor application performance.

#7about 2 minutes

Providing a simplified lab environment for data scientists

A web-based frontend offers pre-configured templates for RStudio and Python, abstracting away infrastructure complexity for data scientists.

#8about 4 minutes

Real-world use cases from tire manufacturing

Several applications are showcased, including real-time tire monitoring for mining trucks, optimizing material mixing, and deploying scrap prediction models to edge devices.

#9about 6 minutes

Integrating the factory into a larger analytics ecosystem

The Data Science Factory is part of a broader ecosystem that includes a telemetry backbone, image recognition pipelines, and predictive maintenance platforms.