Our upcoming release of the omega-ml cloud edition enables deployment of fully multi-cloud #MLOps apps, in just a single command. This works on any cloud, including Microsoft Azure Amazon Web Services Exoscale as well as private clouds. Leveraging #Kubernetes #helm #Terraform

If your task is to deploy a machine learning model and your first thought is to install docker and setup a kubernetes cluster, well you are doing it wrong. Be smart. Use a tool

Awesome post by @Ai4Diversity - thanks for posting! linkedin.com/feed/update/ur…

MLOps for humans: omega-ml with exciting new features: native metric tracking in model training & production, mlflow models & projects, R api for models, datasets, notebooks and scripts, refreshed docs & built-in help. omegaml.github.io/omegaml pypi.org/project/omegam…

#MLOps Monitoring is a key requirement in any production ML or AI system. omega-ml now provides model monitoring and drift detection out of the box. No further tools are required (latest build, new release incoming) buff.ly/4e2OpJQ

Just about to publish a #GenAI extension to my MLOps platform omega-ml. It's just 3 steps to register models, serve and track every call. Ultimately, that's required in any company use case. Step 1: Register the model

A key feature for AI deployment in air-gapped enterprise environments, where arbitrary multi-GB downloads from ollama, huggingface et al. is not permissible: model deployment via OCI registries. For this omega-ml is about to get native OCI registry support, including autosync