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.