How to Build a ML Platform Efficiently Using Open-Source

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Published Jul 15, 2021

Fast-growing startups usually face a common set of challenges when employing machine learning. Data scientists are expected to work on new products and develop new models as well as iterate on existing ones. Once in production, models should be continuously monitored and regularly maintained as the infrastructure evolves. Before too long, data scientists end up spending most of their time doing maintenance and firefighting of existing models instead of creating new ones.

At GetYourGuide, we faced these challenges and decided to think about machine learning development holistically, which led us to our machine learning platform. Our platform uses MLflow to keep track of our machine learning life-cycle and ease the development experience. To integrate our models into our production environment, we also need to deal with additional requirements like API specification, SLOs and monitoring. To empower our data scientists, we have built a templating system that takes care of the heavy lifting of going to production, leveraging software engineering tools and ML-specific ones like BentoML.
In this talk we will present:
– Our previous approaches for deploying models and their tradeoffs
– Our data science and platform principles
– The main functionalities of our platform
– A live demo to create a new service
– Our learnings in the process

Connect with us:
Website: https://databricks.com
Facebook: https://www.facebook.com/databricksinc
Twitter: https://twitter.com/databricks
LinkedIn: https://www.linkedin.com/company/databricks
Instagram: https://www.instagram.com/databricksinc/

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How to Build a ML Platform Efficiently Using Open-Source

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