Friday, April 6, 2018

Low-Code IoT Anomaly Detection with ActiveEon Workflows

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.

Thursday, February 8, 2018

Why Docker for Automation and Analytics

Since the introduction of Docker and its rapid growth, technologies have focused on continuous delivery, integration, etc. or orchestration of containers with two main leaders Kubernetes and Swarm. In early 2015, Activeeon has started working on building more and more integrated features to fit its unique use cases and leverage the core values of containers.

Indeed, most Activeeon users support business line needs for scheduling regular jobs, automating processes, improving the speed of analytics, etc. The objective is to provide consistent and reliable execution no matter the environment. Activeeon and Docker consequently share the same goal and benefit from each other technology.

Docker for Scheduling, Automation, Analytics, etc.

With the rapid growth of the cloud, the computing resource is evolving and may impact the actual execution. Activeeon has an edge to face this trend since it includes a Resource Manager and focus on abstracting away the resource. With Docker containers, Activeeon also enables business lines to execute their jobs within an environment containing the relevant libraries. Thus, it also leverages the values of consistency, reliability and fast startup time from Docker.

To clarify, if a job executes once a semester and require specific packages, Activeeon will abstract away the computing resource and libraries will be included in a specific Docker image. IT operation can then change provider and/or leverage multi-cloud strategies without impacting the job execution.

In conclusion, with Docker and Activeeon, business line users focus on automating and improving time to result in their processes and analytics while IT operation is evolving.

Let’s get technical now

Tuesday, January 16, 2018

Machine Learning Industrialization

The Machine Learning Open Studio (ML-OS) from Activeeon is a complete platform for machine learning industrialization. The main objective is to improve the time to automate, deploy and govern/control workflows and execution.

Simplified Deployment and Integration

Data scientists and devops engineers have developed different interests and skills over time. A platform which is aimed for deployment needs to acknowledge it and develop interfaces to support each role. This section presents a few features to ease the deployment tasks for data scientists.