05-27, 13:30–14:00 (Europe/Vilnius), PyData Room
Deploying a machine learning model to production the right way is not a trivial task, and involves many components. This talk aims first to walk the listener through the realm of MLOps by reviewing the typical challenges faced when deploying machine learning models, and then to flesh out a mature MLOps setup using the Databricks platform.
In recent years, the ability to develop complex machine learning models has immensely progressed. Together with such advances came the need to operationalize models in a mature way, a topic referred to as "MLOps". In March 2020, Google issued its famous 3 levels of MLOps maturity, from the most basic to advanced machine learning software development and deployment lifecycle. However nowadays many companies still struggle to deploy their machine learning models in a mature way.
The first part of the talk will detail what constitutes a mature MLOps and describe essential components such as the feature store, the model registry, the CI/CD pipeline, or the continuous model retraining. The second part of the talk will flesh out these concepts with a batch use case using the Databricks platform.
python, data science, machine learning, dev ops, best practices
I am working as a freelance consultant MLOps engineer at Deloitte. I have worked 6+ years in banking world, after having completed my PhD in Astrophysics. I am very interested in machine learning and cloud technologies such as the ones developed at Databricks (usually on Azure and AWS), and enjoy experimenting new things. Outside of work, I love doing some stargazing out there in the cold weather of Lithuania :)