Khalil Adib Explores Amazon SageMaker and YOLOv5 for Object Detection and Tracking
Meet Khalil Adib, one of Firemind's Data Scientists, with a passion for artificial intelligence (AI) and machine learning (ML). Customer projects aside, Khalil has been experimenting with AWS services such as Amazon SageMaker, and API additions of YOLOv5, for object detection and tracking. Read on below to find out more about this collaboration of services, and how they can be used for a number of smart applications across multiple industries and sectors.
Reduce the Cost and Complexity of Machine Learning Preprocessing
A week ago, Nate Bachmeier, AWS Senior Solutions Architect, and Marvin Fernandes, Solutions Architect, wrote an insightful article over on the AWS Machine learning Blog. Within it, they detailed a solution overview that enabled both reduced complexity and costs, during Machine Learning preprocessing! That statement there really caught our attention. Let’s dig deeper!
Amazon SageMaker Canvas announced at AWS re:Invent
The ability to build systems that can predict business outcomes has become very important within the last few years. This ability lets you solve problems and move faster by automating slow processes and embedding intelligence in almost all IT systems.
Amazon SageMaker Model Monitor – Detecting Language Data Drift
It’s been a few months since we last spoke about language data and the AWS tools made to navigate NLP (Natural Language Processing) data drift. Within that time, the specialist AI/ML Solutions Architects within the Amazon SageMaker team have refined Amazon SageMaker Model Monitor to better detect and combat this drift. In this AWS insight, we look at an AWS approach to detecting data drift in text data, as well as running through some of the basic terminology.