I recently received my AWS DeepLens device. I’m by no means a machine learning expert. However, Andy Jassy’s announcement of the device at 2017’s AWS re:Invent implied that the DeepLens would put Machine Learning and Computer Vision in the hands of non-experts and make it easy. So, let’s try out one of AWS’s pre-trained samples to see just how easy this device is to use.
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One of the great leveling factors of cloud technology is the ability for small companies and individuals to do things that, not that long ago, would have required the support of a larger IT organization’s infrastructure, and doing them quickly and affordably. One of the most powerful yet simple of these technologies is ad hoc querying of data offered by Amazon Athena.
DevOps as a practice has largely grown from the need to manage infrastructure and configuration for large scale applications. Frequently this has led to the technical choice of fleets of Linux-based application servers operating in a dynamic environment such as VMWare or AWS. The reasons for this technical choice are straightforward. Linux enjoys a lightweight and standardized remote administration mechanism through Bash and SSH. Software installation and dependency management are usually a breeze thanks to Linux distributions’ package managers.
Last summer I wrote a piece discussing the hows and whys of using automation to manage cloud infrastructure. I took a high-level approach to the subject, and today I want to dive into how to practically apply this technique in a production environment. I’ll take a simple but flexible example case, using tools from the HashiCorp suite, including Packer and Terraform, to deploy and manage a single simple-case application stack from Jenkins.
This post assumes some basic knowledge of continuous integration, and an application stored in a git repository that has automated test and build jobs using Jenkins or something similar. To this existing basic workflow, we’ll add a simple infrastructure configuration directly into the repository.