Ai-WorkStation &

Enabling Zero Learning Curve, Open-Source, Virtualized Notebooks, Curated Algorithm Notebooks, Model Zoo Notebooks

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The Zeblok Ai-WorkStation provides a single unified environment, which seamlessly orchestrates infrastructure, open source Ai/ML frameworks and proven, original Ai algorithms. An intuitive and simple user interface enables a data scientist, data engineer, or Ai engineer to access petaFLOPS of computing power, efficiently develop and train new Ai models and take them to production in a runtime environment on the same platform, with consummate ease.

The Zeblok Ai-WorkStation builds upon and virtualizes the ubiquitous Jupyter notebook technology, integrating a powerful multi-class orchestration and scheduling layer to support a variety of workloads, from a single GPU to hundreds. Under the hood, our container-based orchestration engine supports both straightforward (non-computationally intensive) AI model development, as well as computationally-intensive workloads.

The Zeblok Ai-WorkStation  provides enormous freedom to run enterprise workloads in many data centers based on cost profile and tailored to application requirements. This flexibility to drive their workloads is capital-efficient. A user can select low latency compute resources from business partners' data centers or from (albeit higher priced) public clouds like AWS, GCP and Azure.

Data scientists need not waste time optimizing frameworks on GPU platforms. Out-of-the-box CUDA optimization for popular Ai frameworks is available and ready to go with the click of a button. Popular data science language bindings, such as R, Scala, and Python are also available. We leveraged a community of data scientists to beta-test and harden these containers so enterprise developers can spend their time developing innovative models, rather than attending to infrastructure.

The Zeblok Ai-WorkStation also makes it easy for developers to build and share new containers with fellow team members, improving organizational productivity. Dependency management, which is involved in Ai development, is greatly simplified and incorporated with examples for a developer to implement high-quality software. Zeblok's Ai-WorkStation also makes it a breeze to promote models developed into APIs via Zeblok's Ai-API™ engine through a few simple commands.

Data Science Frameworks

Ai-MicroCloud™ places all familiar open-source data science frameworks at your fingertips, e.g. Jupyter Notebook, R Notebook, PySpark Notebook, Nvidia RAPIDS Notebook, TensorFlow Notebook, C++ Notebook etc. 



Zeblok’s Ai-AppStore Marketplace provides easy access to a growing library of proven, original AI algorithms. Our curation process, including closed loop validation, ensures high quality, providing algorithm creators with a means of commercialization not previously available. We ensure that algorithms are easy to read, easy to use and easy to share. These include:

  • Ai-Rover™ Notebook: Multivariate Low-Code Data Comprehension/Visualization Tool

  • Ai-Rover™ Notebook For Time Series Data: No-Code Automated Predictive Model Builder 


Zeblok enables you to seamlessly scale to high-performance computing (HPC). Access HPC resources via a Jupyter notebook.

One-click scalability for Ai workloads.


Model Zoo

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Algorithms created and tested on Zeblok’s Ai-MicroCloud™  by independent Ai software vendors (ISVs), Ai startups and renowned academics are available in the Model Zoo for trial and commercialization purposes.  

Command Line Interface Within a Notebook

In the Jupyter notebook command mode, you can edit the notebook as a whole, but not type into individual cells. The keyboard is mapped to a set of shortcuts that let you perform notebook and cell actions efficiently. Enter command mode by pressing Esc or using the mouse to click outside a cell's editor area.


Infrastructure Plan Selection


All Ai-WorkStation’s need underlying infrastructure resources such as vCPUs, RAM, storage, GPUs to compile code. Zeblok’s Ai-MicroCloud™ Manager provides the interface to create different infrastructure plans within your environment as needed. Before a notebook is spawned, users select the appropriate plan.