Hyperopt
Python library for hyperparameter search using random, TPE and adaptive algorithms, installed alongside SciPy and a Jupyter environment for distributed trials.
Platform for natural language data work, letting teams cluster, label and curate conversational utterances into intents and training sets for NLU models. 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.
Python library for hyperparameter search using random, TPE and adaptive algorithms, installed alongside SciPy and a Jupyter environment for distributed trials.
Tooling and SDK access for Google's Imagen text-to-image models, covering image generation, editing, upscaling and captioning through Vertex AI endpoints.
Microsoft framework for model-based machine learning that lets .NET developers describe probabilistic graphical models and run Bayesian inference over them.
Cross-lingual pretrained language model from Microsoft trained with information-theoretic objectives, used for multilingual classification and retrieval after fine-tuning.
Toolkit for optimising and running deep learning inference on Intel CPUs and integrated GPUs, including model conversion, quantisation and the runtime.
Stable Diffusion interface with a node-based canvas, inpainting, ControlNet support and model management, served as a web application on GPU instances.
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.