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.
Joshua Arvin Lat (Arvs) is the Chief Technology Officer of NuWorks Interactive Labs. He previously served as the CTO of 3 Australian-owned companies and startups. He is an AWS Machine Learning Hero and has spearheaded and led the Machine Learning Zero-to-Hero online international events. Years ago, he and his team won 1st place in a global cybersecurity competition with their published research paper on secure two-factor authentication systems. These past couple of years, he has been sharing his knowledge in several international conferences and events to discuss practical strategies for companies and professionals. He is also the author of a Machine Learning and Machine Learning Engineering book called Amazon SageMaker Cookbook: Practical Solutions for Developers, Data Scientists, and Machine Learning Engineers using R and Python (to be released this year [2021])
