Forecasting library from Meta that fits additive time-series models with trend, seasonality and holiday effects, callable from Python and R. Built and maintained by WIEWAVE for Azure Marketplace, AWS Marketplace and Google Cloud Marketplace, on Ubuntu, Debian and Red Hat Enterprise Linux.
| Built & maintained by | WIEWAVE |
|---|---|
| Category | AI & Machine Learning |
| Operating systems | Ubuntu, Debian, Red Hat Enterprise 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 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.
Modular Python machine learning library covering neural networks, reinforcement learning and unsupervised algorithms; the project is no longer actively maintained.
Low-code Python machine learning library automating preprocessing, model comparison, tuning and deployment across classification, regression, clustering and time-series tasks.
Probabilistic programming library for Bayesian modelling in Python, running MCMC and variational inference on a PyTensor computational backend.
Earlier PyMC release built on Theano, providing Bayesian statistical models, NUTS sampling and variational inference for Python data analysis.
Python package that extracts shape, intensity and texture radiomics features from medical images and segmentation masks for quantitative imaging research.
Python environment wrapping DeepMind's StarCraft II Learning Environment, exposing observations and actions for training reinforcement learning agents against the game API.
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.
