Deep Learning Deployment/Continuous Integration and Delivery Ipython Machine-Learning Open-Source
See in schedule Download/View SlidesContext: Today is relatively easy to create and train a conversational agent using Machine Learning Techniques, fire it up and showcase it in your computer
Problem: Sharing your chatbot with the outside world is not as easy as training your models. Load Balancer, Unit Test, Integration Tests, Differential Tests ... Text Analytics and retrain the models to better serve your audience goes way beyond the simple agent that runs in the developer environment
Solution: I want to show how from my experience of deploying bots to production, leveraging DevOps + DataScience skills along with an entry level knowledge of Databases, CI/CD and distributed systems you can take your prototypes to a next level, deploy, iterate and re-train your models faster.
Pre-reqs: Entry level understanding of CI/CD Pipelines, NLP, jupyterhub, Version Control, Rasa
Type: Talk (30 mins); Python level: Intermediate; Domain level: Intermediate
Born and raised in Colombia, studied Electronic Engineering and specialized in Digital Electronics, former exchange student in Czech Technical University in the faculty of Data Science. I started my career working in Intel as Field Engineer, then moved to Oracle in Bogota. After 5 years in Oracle as Solution Architect I decided to get deeper in my knowledge of Machine Learning and Databases, relocated to Prague in Czech Republic, where I studied Data Science for a while from the academia perspective, later jumped to Solution Architect in CA technologies, later moved on to Developer of Automations and NLP in Adecco. Currently I work at Gitlab as Technical Marketing Engineer, teaching and evangelizing developers about DevOps and MLOps
