Machine Learning as a Service

Experimenting with machine learning algorithms or integrating such techniques into an existing environment often presents challenges, like selecting and deploying the right infrastructure, in addition to having the necessary data science background and skills, etc. In this post, we present a service that allows users to train machine learning models, run analyses using trained models, as well as manage data required for such models or analyses. Now machine learning models or the prediction results can be easily integrated in to existing continuous integration (CI) or IT infrastructure using REST API.

Overview

The main components of such a service are Apache OpenWhisk, Red Hat OpenShift, and Ceph Storage. These components are available under AI Library at https://gitlab.com/opendatahub/ai-library. OpenWhisk is a serverless computing platform that provides the interface through which users can submit HTTP requests to train or execute machine learning models. HTTP requests submitted to OpenWhisk are actually targeted to stateless functions, called actions, that run on the platform. Ceph Storage is used for storage of training and prediction data, models and results. Users can submit data in to Ceph backend through OpenWhisk actions provided in our implementation (s3.py) or any custom tools such as RADOS object storage utility that can interact with Ceph storage. The action ‘s3.py’ not only supports Ceph, but also any S3-compatible storage backend.

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Seeing the Trees in the Forest: Anomaly Detection with Prometheus

Red Hat’s work within the field of artificial intelligence is primarily taking three directions right now. First, our engineers see the inclusion of AI features as a workload requirement for our platforms, as well as AI being applicable to Red Hat’s existing core business in order to increase open source development and production efficiency. In short, Red Hat thinks AI can be good for our customers and good for us, too.

Second, Red Hat is collaborating with the Mass Open Cloud project to establish the one thing that all AI tools need the most: data. Our team members are working on the Open Data Hub, a cloud platform that lets data scientists spend less time on dealing with infrastructure administration and more time building and running their data models.

The third aspect of Red Hat’s work in AI right now is at the application level. More to the point, how can developers plug in AI tools to applications so that data from those applications can be gathered for storage and later modeling?

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The Future of Storage in Container Space: Part 4

The challenges of maintaining persistent storage in environments that are anything but persistent should not be taken lightly. My recent conversation with Ceph founder Sage Weil certainly made that clear. Thus far, the conversation with Sage has highlighted key areas of focus for the Red Hat Storage team as they look to the horizon, including how storage plans are affected by:

  • Hardware trends (examined in Part 1)
  • Software platforms (reviewed in Part 2)
  • Multi-cloud and hybrid cloud (discussed in Part 3)

In the last segment of our interview, Sage focused on technology that’s very much on the horizon: the emerging workloads. Specifically, how will storage work in a world where artificial intelligence and machine learning begins to shape software, hardware, and networking architecture?

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10 Trends Reshaping the Developer Experience

In this video, Director of Product Management for Developer Tools Brad Micklea talks through ten trends Red Hat is investing in that are already reshaping the developer experience.

The idea of software development as a major creative source for innovation has emerged in recent decades.  During most of that time for the people writing code and running it in production, instead of being deep in the act of painting a masterpiece, they have had to spend too much time building and cleaning brushes.

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