On October 8 Julien Simon will hold an online meetup, where he will tell about Amazon SageMaker, a fully-managed and modular service for ML, and what capability lets you easily run and your data processing workloads without having to worry about infrastructure at all.
As ML practitioners know, transforming raw data into a dataset ready for training is hard work. Converting data to the format expected by the algorithm, splitting and shuffling data, handling outliers, filling missing values, engineering new features: the list goes on! Indeed, running, scaling, and keeping track of these processing jobs can quickly add lots of extra cost and complexity to any ML project. In this session, we’ll start with a quick introduction of Amazon SageMaker, a fully-managed and modular service for ML. Then, we’ll discuss SageMaker Processing, a capability that lets you easily run and your data processing workloads without having to worry about infrastructure at all. We’ll also talk about SageMaker Experiments, another capability that makes it easy for you to organize, and track ML jobs at any scale. Of course, we’ll do a demo using Jupyter notebooks. This talk should be of interest to any ML developer and data scientist. Attendees should be reasonably familiar with Python as well as typical ML workflows. Basic knowledge on core AWS services (regions, EC2, S3, IAM) would be nice but isn’t mandatory.
10% of the money for the purchased tickets will be donated for the Charity campaign of Group of Active Rehabilitation.
Report language: English
?For 9th Data Science UA Online Conference attendees, participation in the webinar is free ? Just email us if you want to attend the event on email@example.com