One platform to build, fine-tune, and deploy ML models. No MLOps team required.
Access Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
Try Free
Full-stack observability with actually useful AI | Grafana Cloud
Our generous forever free tier includes the full platform, including the AI Assistant, for 3 users with 10k metrics, 50GB logs, and 50GB traces.
Built on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
Fast, flexible and easy to use probabilistic modelling in Python
...The modular implementation allows one to easily drop normal distributions into a mixture model to create a Gaussian mixture model just as easily as dropping a gamma and a Poisson distribution into a mixture model to create a heterogeneous mixture. But that's not all! Because each model is treated as a probability distribution, Bayesian networks can be dropped into a mixture just as easily as a normal distribution, and hidden Markov models can be dropped into Bayes classifiers to make a classifier over sequences. Together, these two design choices enable a flexibility not seen in any other probabilistic modeling package.