Wednesday, July 18, 2018

Analytics on Energy Consumption

The energy industry is evolving to face new challenges such as population growth, clean energy, consumption growth with electric vehicles, etc. Analyzing user consumption is key to plan the future.

Recently, Activeeon acquired real user consumption data from a lead. We took this opportunity to leverage our solution and analyze it.

In this quick video, we will present 4 different steps for a proper analysis:

  • Performing data fusion: Merging different data sources into one usable one with Activeeon embedded data connectors
  • Clustering electrical consumption: Gathering user consumption pattern in multiple groups
  • Detecting anomalies in electricity consumption: Automatically detect anomalies based on each user's normal behavior
  • Analyzing electricity consumption with ELK stack: Manage the lifecycle of ELK for real time data analysis

Contact us to get more information or visit our website

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.

Tuesday, December 5, 2017

Agility for Data Scientists

The process followed by data scientists can be simplified by the diagram below. As shown, it is an iterative process where hypothesis are made and the model improved incrementally. Multiple platforms are competing on supporting data scientists’ needs but too often focus on standard workflows and methods whereas each use case is different and require specific development.

At Activeeon, our data scientist team is focusing on building templates that can be used and tweaked easily at each iteration.

Monday, November 27, 2017

Scheduling Recurring Jobs - Best Practices

This article aims to briefly present the best practices and suggest tools to plan and monitor recurring jobs with Activeeon’s solution. The main concerns are addressed through different features and services:

  • Schedule management through the Job Planner service,
  • Workflow validity and management through the Catalog,
  • Notification on event with an integrated feature to be more proactive,
  • Requirements checks prior to execution through a selection script to avoid unnecessary issues.

Job Planner

The Job Planner is a service included in ProActive to manage recurring jobs. The main benefits are:

  • Dedicated and centralized interface,
  • Clear forecast of workflows,
  • Simplified management of exceptions: additional executions and/or exclusion periods.

Generate cron expressions

Monday, November 20, 2017

Activeeon supports Python natively

In an objective to build an open platform for Machine Learning workflows and better data analytics, the latest release of Activeeon's solution includes a native Python task.

To keep it to the main benefits:

  • Analyzing data in Python using numpy, pandas, TensorFlow, etc. is now greatly simplified.
  • Native Python tasks run 10 to 100 times faster than Jython tasks.
  • It fully integrates with existing system such as Generic Information or Variable propagation.
  • Multiple Python versions are supported , even within the same workflow.

How to