Organizing infrastructure for ML applications is a challenging task. Depending on your pipeline you may need to combine different types of virtual machines, organize research infrastructure as well as monitor the system. You may also need to have a way of maintaining ML infrastructure so it’s to deploy updates. Here are the top 5talks from AWS Summit which would help you to stay in the loop regarding current trends.
AWS Summit is free and sessions can be watched on-demand. Feel free to register at the link.
AIM301 Implement MLOps practices with Amazon SageMaker
This session will showcase MLOps practices and…
MLOps is an emerging field of best practices for businesses to run ML workflows in production. There are multiple challenges associated with MLOps which makes it different from traditional DevOps. From monitoring where you may need to monitor model performance and change in data to cost and scale optimizations where you need to optimize for GPU idle time. New tools and approaches which help to tackle the challenges appear every month so here are the top 5 AWS re:Invent 2020 talks to help you stay in the loop of MLOps in the AWS cloud.
Satellite imagery is becoming a more and more important source of insights about changes that happen worldwide. There are multiple satellites that provide publicly available data with almost full earth coverage and almost weekly frequency.
One of the main challenges with satellite imagery is to deal with getting insights from the large dataset which gets continuous updates. In this blog post, I want to showcase how you can use Amazon Rekognition custom labels to train a model that will produce insights based on Sentinel-2 satellite imagery which is publicly available on AWS.
The Sentinel-2 mission is a land monitoring constellation…