90DaysOfDevOps VS amazon-sqs-java-messaging-lib

90DaysOfDevOps 2022

I am trying to get into DevOps following https://github.com/MichaelCade/90DaysOfDevOps/tree/main

Sorry I don't have any particular project atm. However, you can take a look at this: https://github.com/MichaelCade/90DaysOfDevOps/blob/main/2022.md

I've been contributing to an Online challenge called #90DaysOfDevOps for its second "season". This was started in 2022 by a friend who was changing roles and wanted to accelerate his learning journey. I wrote a section on Red Hat OpenShift, as I went through learning more about how it works and operates.

90DaysOfDevOps

*This is part of a series that will be covered here, but I also encourage you to follow along with the rest of the series on 90DaysOfDevOps.

*As a quick disclaimer - as I am an AWS Serverless Hero, most of the examples and explanations I give will reference the AWS ecosystem since that is where my expertise is. Many of the AWS services and tools we will discuss have equivalents across Azure, GCP, or other tooling. I will do my best to call these out going forward. This is part of a series that will be covered here, as well as part of a larger effort on 90DaysOfDevOps, which I highly encourage you to follow along with.

Save to SQS (i.e. save each Event to Amazon’s SQS queuing service)

Amazon SQS

Messaging/Queueing Systems (Amazon SQS, RabbitMQ, Beanstalkd)

The last stage is productionizing the model. The goal of this phase is to create a system to process each image/video, gather the relevant features and inputs to the models, integrate the models into a hosting service, and relay the corresponding model predictions to downstream consumers like the MCF system. We used an existing Safety service, Content Classification Service, to implement the aforementioned system and added two specialized queues for our processing and various service integrations. To use the model for online, synchronous inference, we added it to Gazette, Reddit’s internal ML inference service. Once all the components were up and running, our final step was to run A/B tests on Reddit to understand the live impact on areas like user engagement before finalizing the entire detection system.