Sequential model-based optimization library for Python that performs Bayesian hyperparameter search, built on top of scikit-learn, NumPy and SciPy. Built and maintained by WIEWAVE for Azure Marketplace, on Ubuntu and Debian.
| Built & maintained by | WIEWAVE |
|---|---|
| Category | AI & Machine Learning |
| Operating systems | Ubuntu, Debian |
| Marketplaces | Azure Marketplace |
| Offer types | Public listing, with private offers on request |
| Marketplace | Status |
|---|---|
| Azure Marketplace | Published |
| AWS Marketplace | Available on request |
| Google Cloud Marketplace | Available on request |
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.
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.
Adds numerical routines for integration, optimisation, linear algebra, signal processing and statistics on top of NumPy in a prepared Python environment.
Statistical plotting library layered over Matplotlib, shipped with pandas and Jupyter so heatmaps, distribution and regression charts can be produced immediately.
Python workspace preloaded with NLP libraries and pretrained models for classifying text polarity, including tokenization, feature extraction and evaluation notebooks.
Collection of machine learning algorithms implemented in C++ with bindings for Python, R and Octave, focused on kernel methods and support vector machines.
Wrapper exposing PyTorch models through the scikit-learn estimator API, so grid search, pipelines and callbacks work directly with neural networks.
Data-centric AI platform where labelling functions and weak supervision generate training data programmatically instead of relying on manual annotation.
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.
