The IoT world and datacenter world are known to generate information about anything from sensor data to software logs. This data carries information about environmental parameters, system health, software behavior, etc. and their previous state. It can consequently be used to train predictive models and alert IT operation in case of current and potential future issues. This predictive maintenance model or predictive analytics help companies prevent issues and save money.
In this specific blog post, we will focus on the parallelization of model training for machine learning. It will be done at scale with low-code, and little or no need to configure the underlying infrastructure. We will take you through the main steps to setup a training workflow, edit in seconds the training model, the input, the output and finally we will parallelize it with Activeeon Workflow & Scheduling.
The current blog complement a previous one, showing how we can achieve a simple scalable solution with fewer code.
To keep it simple, as shown below, we will focus on IoT sensors, simulated by tasks that generates random logs at fixed intervals.
Grey arrows represent the flow of the data
Orange arrows represent the interaction of Activeeon Workflow & Scheduling with the various components.
