Jupyter Notebook and JupyterLab served on a Python stack with NumPy, pandas, scikit-learn and related libraries for interactive model development. Built and maintained by WIEWAVE for Azure Marketplace, AWS Marketplace and Google Cloud Marketplace, on Ubuntu, Debian, Red Hat Enterprise Linux, AlmaLinux and Rocky Linux.
| Built & maintained by | WIEWAVE |
|---|---|
| Category | AI & Machine Learning |
| Operating systems | Ubuntu, Debian, Red Hat Enterprise Linux, AlmaLinux, Rocky Linux |
| Marketplaces | Azure Marketplace, AWS Marketplace and Google Cloud Marketplace |
| Offer types | Public listing, with private offers on request |
| Marketplace | Status |
|---|---|
| Azure Marketplace | Published |
| AWS Marketplace | Published |
| Google Cloud Marketplace | Published |
Need it on another marketplace, or as a private offer for your organisation? cloud@wiewave.com
Each image follows its distribution's own provisioning model, package manager and security tooling — not one build relabelled several times.
LTS and interim releases, Minimal and Pro variants, built to Canonical's cloud-image conventions.
Stable and oldstable, with backports where a workload needs a newer runtime than the release ships.
RHEL 8 and 9 images, including BYOS and pay-as-you-go licensing models on each marketplace.
The 1:1 RHEL-compatible rebuild, and the default landing spot for teams migrating off CentOS Linux.
Enterprise-grade RHEL compatibility with a community governance model, on 8 and 9 streams.
The same four steps behind every offer we've published, including the hardening and CIS Benchmark checks every build goes through.
The distribution, licensing model and target marketplaces are agreed before anything is built.
Packer templates, Ansible provisioning and a pinned package set — then hardened, scanned and checked against the CIS Benchmark for its distribution.
Taken through each cloud's own certification pipeline before it goes live on the marketplace.
Rebuilt on the upstream security cadence and re-published, with old versions retired without breaking deployments.
If yours isn't here, ask our marketplace team directly.
GPU-ready training and inference images with drivers, CUDA and frameworks already matched to each other.
PyTorch with a CUDA build matched to the installed driver, so training starts on the GPU instead of silently falling back to CPU.
Python framework for computational imaging and inverse problems, providing composable linear operators and proximal optimization algorithms that run on CPU or GPU.
Preconfigured environment for packaging trained models into HTTP inference endpoints, bundling common Python serving frameworks and their dependencies for quick deployment.
Ray with the head and worker roles scripted, for distributing training, tuning and batch inference across a cluster.
Meta's applied reinforcement learning platform, formerly Horizon, built on PyTorch for training and evaluating decision policies from logged production data.
Stack for observing deployed machine learning models in production, collecting prediction data to track drift, data quality and accuracy over time.
Tell us the distribution, the marketplace and the commercial model — we'll build, certify and publish it as a public listing or a private offer.
