Scale By the Bay Scale By the Bay

Thursday, October 28, 2021

- PDT
Designing and Building Complex Machine Learning Engineering Projects and Workflows: Serverless x Containers
Joshua Arvin Lat
Joshua Arvin Lat
NuWorks Interactive Labs, Chief Technology Officer

Over the past couple of years, several professionals and teams have started to utilize serverless and container concepts and techniques to design and build scalable, low cost, and maintainable applications. Understanding the concepts alone will not guarantee success especially when dealing with modern complex requirements involving Machine Learning and Data Engineering. In this talk, we will talk about how to use different tools and services to perform machine learning experiments ranging from fully abstracted to fully customized solutions. These include performing automated hyperparameter optimization and bias detection when dealing with intermediate requirements and objectives. We will also show how these are done with different ML libraries and frameworks such as scikit-learn, PyTorch, TensorFlow, Keras, MXNet, and more. In addition to these, I will also share some of the risks and common mistakes Machine Learning Engineers must avoid to help bridge the gap between reality and expectations. While discussing these topics, we will show how containerization and serverless engineering helps solve our technical requirements.