Scaling Machine Learning from 0 to Millions of Users
Scaling Machine Learning from 0 to Millions of Users


Data scientists and machine learning engineers use a variety of open source projects in their everyday tasks: scikit-learn, SparkML, TensorFlow, Apache MXNet, Pytorch, etc. They make it very easy to get started, but as models become more complex and datasets become larger, training time and prediction latency become a significant concern. Here too, containers can help, especially when used with elastic on-demand compute services. In this session, we'll show you how to scale machine learning workloads using containers on AWS (Deep Learning AMI and containers, ECS, EKS, SageMaker). We'll discuss the pros and cons of these different services from a technical, operational and cost perspective. Of course, we'll run some demos.


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.

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google