Preparing your data for machine learning workloads
Machine learning outcomes are only as good as the data they are built on, but preparing data for these advance workloads can be time-consuming and difficult to scale, especially if you are looking to implement machine-learning applications that rely on data from across your entire organization. In this session, Ben Snively will share some best practices related to collecting, storing, and processing big data and disparate data sets so that you glean intelligent insights from your machine-learning algorithms. We will share some architectural design patterns.
Session ID: LT1614 Presentation Type: Live Session (Replay Available)
Date / Time: [Day 1] Mon. Sep. 14, 2020 @ 16:15 ET (US)
Presented by:AWS is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from datacenters globally. Millions of customers are using AWS to lower costs, become more agile, and innovate faster. Click for more details and additional sessions and content.
To view this session, register for the conference.