The Conference for Machine Learning Innovation

Continuous Delivery for Machine Learning Applications with Open Source Tools

Session
Join the ML Revolution!
Until Conference starts:
✓Special discount for Freelancers
✓10% Team Discount
Register Now
Join the ML Revolution!
Until Conference starts:
✓Special discount for Freelancers
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
Register Now
Infos
Tuesday, December 10 2019
10:15 - 11:00
Room:
Saal A

Developing intelligent applications is becoming easier based on huge amounts of freely accessible documentation, tutorials, and software frameworks. But what does it actually take to bring these applications into production environments, run them scalable and in high quality and improve them continuously?
We will present how we have developed a chatbot for HR services with rasa.ai, TensorFlow and Keras and organized the development process with mixed teams of product owners, data scientists, dialog designers, data engineers, and machine learning developers with a continuous delivery approach. By using pipelines and version control mechanisms for the different artifacts (code, data, models, parameters), quality gates, and continuous delivery orchestration, we were able to automate the process of continuously developing and improving the chatbot. This enabled us to bring models continuously from the data scientists notebook into production, improving the chatbot and experimenting with live users. The audience will learn how to apply Continuous Delivery for Machine Learning and the great benefits they will get. In the end, we will give an outlook for future developments in software engineering practices for machine learning applications.

Behind the Tracks