Amazon SageMaker offers a highly customizable platform for machine learning at scale. Job execution within Amazon SageMaker can take some time to set up, which can be inconvenient or even time consuming during development and debugging phases. Running training and processing jobs locally can greatly increase the speed of development and debugging before running them at scale on AWS.
Up to a few years back, writing Dockerfiles was easy. In many cases, it still is - unless you are working with a mixed fleet of Intel and ARM-based processors. Are you familiar with this situation and you do not want to maintain two almost identical Dockerfiles? There is a solution…
The new agentless collector for the Application Discovery Service can be used to collect data from VMware vCenter and thus is useful whenever the ADS agent cannot be installed on the servers. However, AWS “forgot to mention” how to ensure connectivity over a proxy server in environments that do not allow direct internet access. Here`s how I fixed the issue.
Database migrations can be a complex and time consuming task. This is particularly true for heterogeneous database migrations. Today we will see how the AWS Database Migration Service (DMS) can be a life saver for both homogeneous and heterogeneous migrations.
Running Shiny on Fargate Some guys still thinking R cannot be used at scale or only in a limited way. I still do not understand the reason why people are like this. Since my last post about AWS Batch, which is a Docker-based service within AWS, which enables you to work with R, I spend a lot of time with Fargate, another Docker-based service on AWS. This time my boss wanted a BI app.
