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Introducing CORE community platform

A free community platform bringing data scientists back to their CORE

The data science community has been central to the rapid growth of AI and machine learning innovation. Before its hype across the industry, data science was an unexplored pasture for enthusiasts to gain deeper insights combining the power of math, statistics and computer science. What once was a way of tinkering with algorithms, has become widely embraced by the corporate world as a competitive strategy. With the growing technical complexity of the AI field, the data science community has strayed from the core of what makes data science such a captivating profession- the algorithms. Today’s reality is that data scientists are spending 80% of their time on non-data science tasks, and 65% of models don’t make it to production. CORE opens its end-to-end solution to the community to help data scientists and engineers to focus less on technical complexity and DevOps, and more on the core of data science – solving complex problems. helps leverage both on premise or off premise compute, enabling enterprise IT to support the full lifecycle of AI development. CORE makes AI innovation more accessible, by simplifying MLOps with one click, and reducing hidden technical debt associated with building machine learning applications. The release is a contribution to strengthen the dedicated data scientists that make AI innovation possible.

What is CORE?

The CORE platform offers a unified code-first workbench built for data scientists. Supplied with workflow management tools, pre-configured containers and cluster orchestration, data scientists can begin exploration quickly. CORE’s flexible infrastructure gives data scientists the control to use any language, AI framework, and compute environment whether on premise or cloud compute. CORE offers everything a data science needs to build high impact ML models from research to production with:

  • ML Workflow Management – end-to-end tracking and monitoring capabilities
  • Interactive Workspaces – use any development environment like JupyterLab, RStudio, and more with pre-installed dependencies and version control
  • Dataset Management – integrate and version any kind of data to reuse in any project, experiment, and/or notebook
  • ML Flows – build production-ready machine learning pipelines with drag and drop
  • AI Frameworks in One-Click – native integration with NGC GPU-optimized containers 
  • Reusable ML Components –  Build and customize a library of reusable ML components 
  • Cluster Orchestration – Hybrid cloud / Multi Cloud capabilities with native Kubernetes and meta-scheduler  
  • One click Deployment – deploy models as  scalable REST APIs based on kubernetes  
  • Production Model Monitoring – monitor and manage production models with Kibana, Grafana and update with continual learning
  • Launch 100s of experiments – track and compare hundreds of experiments with hyperparameter optimization-

How to get CORE

The cnvrg CORE platform can be installed on-premise or in a cloud environment directly here.
Once signed up, you or any other team member can just follow the docs instructions to quickly install core by choosing which infrastructure you’d like to set up Kubernetes and by using Helm.

There are many ways to start with CORE. Users can invite their team members to their organization and start collaborating on projects. Or, you can start with an example project, or build a pipeline with pre-built ML components. Users will be able to connect data sources, run ML experiments at scale and deploy to production with full tracking and monitoring tools.

We welcome the entire data science community to our free ML platform! We look forward to seeing what types of high impact models grow from CORE users. There will be a forum where community members can share projects, ask questions and collaborate on the next generation of AI innovation. 

You’re just a few steps away from doing more DATA SCIENCE. Get started today!

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