Implementation of an intelligent car wash service - Vincit Teatime 2013

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The document outlines the development of an intelligent car wash service over multiple iterations using an agile approach. An initial proof of concept was built in under a month with a simple web interface and third-party license plate recognition. This pilot had 90 customers and provided early lessons. Subsequent versions added more features and scaled the customer base by 400%, though issues remained. The final version involved rebuilding the license plate recognition and washer controller software from scratch, solving reliability problems and enabling fast recognition and platform growth. The project took over a year of development across many iterations to create a satisfied customer and end users.

Implementation of an
intelligent car wash service
Anssi Kuutti
Wednesday, April 10, 13

• Customer: Osakeyhtiö Hallman
• Known for Harald restaurants and
Trube buns
• Washer premises in Kuopio
• Only little experience in software
projects
Case
&Wednesday, April 10, 13

• To minimize the risk in a software project:
• Agile development methods
• Short iterations
• Continuous delivery
• Proof of concept and piloting
• Don’t be afraid of failing, next time you will do
better
What can you learn from this...
Wednesday, April 10, 13

Iterative approach
Vision
TryLearn
Wednesday, April 10, 13

• To build a new modern platform,
web, mobile, etc.
• Changing an age old industry with a
car wash service that has:
• Monthly subscriptions
• License plate recognition
The Vision
Wednesday, April 10, 13

• Less than one month of work
• The most simple of web UI’s
• Third party software to solve the core problem:
license plate recognition
• Simple production environment
• Simple integration to existing washer controller
system
• ~90 pilot customers
The first version: proof of concept
Wednesday, April 10, 13

• The good
• Customers like the simplicity of monthly
subscription
• Certain WOW factor with license plate
recognition
• The bad
• Recognition is a bit slow and people don’t read
instructions
• More sophisticated admin UI is needed
• Network issues are hard to identify
• Washer controller system clearly isn’t designed
for this
Lessons learned vol 1
Wednesday, April 10, 13

Next step
Vision
TryLearn
Vision
TryLearn
Wednesday, April 10, 13

• A few months of work
• New production environment in the cloud
• Full blown admin UI
• Statistics & logs
• Invoicing
• Responsive web UI for customers
• Marketing and credibility
• Registration and account management for
customers
• Wash history
Next step
Wednesday, April 10, 13

• The good
• Able to grow customer base by 400%
• Steady income shows potential in the
business model
• The bad
• Issues still exist with slow recognition and
washer controller software
Lessons learned vol 2
Wednesday, April 10, 13

Next step
Vision
TryLearn
Vision
TryLearn
Vision
TryLearn
Wednesday, April 10, 13

• Get rid of all legacy systems
• Own implementation of the license plate
recognition
• About two months of work
• Only Finnish plates supported at first
• Own implementation of the washer controller
software
• About four months of work
• Works oﬄine as well
Next step
Wednesday, April 10, 13

The washer machine
Wednesday, April 10, 13

The user interface
Wednesday, April 10, 13

• The good
• Reliability issues solved
• Blazing fast recognition
• Platform finally ready for multiplication!
• The bad
• -
Lessons learned vol 3
Wednesday, April 10, 13

Autorekisteri.fi app
Wednesday, April 10, 13

• A year of development
• Three diﬀerent versions and architectures
• Over 20 two week iterations
• About 700 Git commits
• Over 100 production software updates
• Satisfied customer
• Satisfied end users
Summary
Wednesday, April 10, 13

Thanks!
Questions or feedback?
Passionate Team Leader Anssi Kuutti
+358 505307938
anssi.kuutti@vincit.fi
Wednesday, April 10, 13

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Monitoring and Managing Anomaly Detection on OpenShift Overview Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices. Key Topics Covered 1. Introduction to Anomaly Detection - Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems. 2. Understanding Edge (IoT) - Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source. 3. What is ArgoCD? - Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices. 4. Deployment Using ArgoCD for Edge Devices - Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD. 5. Introduction to Apache Kafka and S3 - Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions. 6. Viewing Kafka Messages in the Data Lake - Learn how to view and analyze Kafka messages stored in a data lake for better insights. 7. What is Prometheus? - Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices. 8. Monitoring Application Metrics with Prometheus - Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system. 9. What is Camel K? - Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes. 10. Configuring Camel K Integrations for Data Pipelines - Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow. 11. What is a Jupyter Notebook? - Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text. 12. Jupyter Notebooks with Code Examples - Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.

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Implementation of an intelligent car wash service - Vincit Teatime 2013

1. Wednesday, April 10, 13

2. Implementation of an intelligent car wash service Anssi Kuutti Wednesday, April 10, 13

3. • Customer: Osakeyhtiö Hallman • Known for Harald restaurants and Trube buns • Washer premises in Kuopio • Only little experience in software projects Case &Wednesday, April 10, 13

4. • To minimize the risk in a software project: • Agile development methods • Short iterations • Continuous delivery • Proof of concept and piloting • Don’t be afraid of failing, next time you will do better What can you learn from this... Wednesday, April 10, 13

5. Iterative approach Vision TryLearn Wednesday, April 10, 13

6. • To build a new modern platform, web, mobile, etc. • Changing an age old industry with a car wash service that has: • Monthly subscriptions • License plate recognition The Vision Wednesday, April 10, 13

7. • Less than one month of work • The most simple of web UI’s • Third party software to solve the core problem: license plate recognition • Simple production environment • Simple integration to existing washer controller system • ~90 pilot customers The first version: proof of concept Wednesday, April 10, 13

8. Wednesday, April 10, 13

9. Wednesday, April 10, 13

10. • The good • Customers like the simplicity of monthly subscription • Certain WOW factor with license plate recognition • The bad • Recognition is a bit slow and people don’t read instructions • More sophisticated admin UI is needed • Network issues are hard to identify • Washer controller system clearly isn’t designed for this Lessons learned vol 1 Wednesday, April 10, 13

11. Next step Vision TryLearn Vision TryLearn Wednesday, April 10, 13

12. • A few months of work • New production environment in the cloud • Full blown admin UI • Statistics & logs • Invoicing • Responsive web UI for customers • Marketing and credibility • Registration and account management for customers • Wash history Next step Wednesday, April 10, 13

13. Wednesday, April 10, 13

14. • The good • Able to grow customer base by 400% • Steady income shows potential in the business model • The bad • Issues still exist with slow recognition and washer controller software Lessons learned vol 2 Wednesday, April 10, 13

15. Next step Vision TryLearn Vision TryLearn Vision TryLearn Wednesday, April 10, 13

16. • Get rid of all legacy systems • Own implementation of the license plate recognition • About two months of work • Only Finnish plates supported at first • Own implementation of the washer controller software • About four months of work • Works oﬄine as well Next step Wednesday, April 10, 13

17. The washer machine Wednesday, April 10, 13

18. The user interface Wednesday, April 10, 13

19. Wednesday, April 10, 13

20. • The good • Reliability issues solved • Blazing fast recognition • Platform finally ready for multiplication! • The bad • - Lessons learned vol 3 Wednesday, April 10, 13

21. Live demo... Wednesday, April 10, 13

22. Wednesday, April 10, 13

23. Autorekisteri.fi app Wednesday, April 10, 13

24. Wednesday, April 10, 13

25. • A year of development • Three diﬀerent versions and architectures • Over 20 two week iterations • About 700 Git commits • Over 100 production software updates • Satisfied customer • Satisfied end users Summary Wednesday, April 10, 13

26. Thanks! Questions or feedback? Passionate Team Leader Anssi Kuutti +358 505307938 anssi.kuutti@vincit.fi Wednesday, April 10, 13

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