How to scale machine learning experiments in the cloud? - Shashank Prasanna, AWS
Conference Website: http://saiconference.com/FTC
Shashank Prasanna is an AI & Machine Learning Technical Evangelist at Amazon Web Services (AWS) where he focuses on helping engineers, developers and data scientists solve challenging problems with machine learning. Prior to joining AWS, he worked at NVIDIA, MathWorks (makers of MATLAB & Simulink) and Oracle in product marketing, product management, and software development roles. Shashank holds an M.S. in electrical engineering from Arizona State University.
Abstract: Machine learning involves a lot of experimentation. Data scientists spend several days, weeks or months performing algorithm search, model architecture search, hyperparameter search etc. In this session, we'll discuss the importance of experimentation in the scientific method applied to machine learning. We'll take a look at designing and running experiments to study effect of various factors on accuracy, complexity, robustness and other responses. Through examples, we'll then see how you can easily run large-scale machine learning experiments in the cloud using container technologies such as Amazon Sagemaker and Kubernetes.