Industrialize your Data Science with Managed Kubeflow by using Credo YQ

Tomorrow’s winners are data driven organizations that efficiently deploy and maintain their data science and engineering applications at a large scale. Our vision on how to industrialize data science and our extensive background in IT have crystallized into the ‘YQ’ open data science platform:

  • built for modelling and data experts,
  • to develop and industrialize models,
  • based on robust, secure, cost-efficient, and scalable technologies,
  • constantly adapted to the state-of-the-art in contemporary tooling (hence ‘open’).
yq
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Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly ... it is dangerous to think of these quick wins as coming for free. Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems...

D. Sculey et al. (Hidden Technical Debt in Machine Learning Systems ) Google
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Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly ... it is dangerous to think of these quick wins as coming for free. Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems...

D. Sculey et al. (Hidden Technical Debt in Machine Learning Systems ) Google

Four good reasons to adopt YQ

At its core, YQ is a managed version of the emerging open-source platform Kubeflow, enhanced by a Credo-specific customization and overlay that allows to efficiently develop, deploy and operate models (a.k.a. MLOps).
Why opt for YQ, rather than a closed (Cloud) vendor solution or in-house development? We see four main reasons.

  1. Enjoy the benefits of cutting-edge open-source tooling without suffering the volatility
  2. Drastically shorten your time-to-market, while reducing up-front and recurrent costs
  3. Keep your options open using a platform that can be installed on any environment that runs Kubernetes
  4. Opt for a coherent MLOps user experience from day one

Kubernetes-native

Cloud-agnostic

Powered by
Kubeflow

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“The obvious conclusion: If you’re interested in enterprise IT infrastructure, Kubernetes should be your technology of choice...”

Jason Bloomberg, SiliconANGLE
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The obvious conclusion: If you’re interested in enterprise IT infrastructure, Kubernetes should be your technology of choice...

Jason Bloomberg, SiliconANGLE

Development

Data scientists and engineers have all the open-source champions available at the tips of their fingers in a fully scalable development environment, together with connectors to external SQL databases and other common data analytics tools.

open-source data-science python
open-source data-science R
open-source data-science apche-spark
open-source data-science tensorflow

Deployment

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Transform insights into batch or real-time apps and seamlessly deploy them on a rock-solid scalable production environment using YQ’s highly configurable deployment strategies

Containers

Continuous
integration and
deployment (CI/CD)

Version control

Deployment

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Delivery

Click to enlarge

In batch or real-time, large or small data, complex or simple workflows, manually triggered or automatically scheduled, YQ makes no excuses when it comes to reliable delivery process.

Even more, YQ silently collects delivery-oriented metadata from that are made readily available in the monitoring dashboards.

Batch / real-time

Monitoring
dashboards

Orchestration

Find out how YQ can help your organisation to develop, deploy and deliver data products. Ask for our brochure.

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