omega-ml (@omega_ml) 's Twitter Profile
omega-ml

@omega_ml

MLOps simplified. Swiss made. Open core.

ID: 915692783800811521

linkhttp://www.omegaml.io calendar_today04-10-2017 21:39:07

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258 Following

omega-ml (@omega_ml) 's Twitter Profile Photo

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

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 <a href="/Azure/">Microsoft Azure</a> <a href="/awscloud/">Amazon Web Services</a>
<a href="/Exoscale/">Exoscale</a> as well as private clouds. Leveraging #Kubernetes #helm #Terraform
omega-ml (@omega_ml) 's Twitter Profile Photo

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

omega-ml (@omega_ml) 's Twitter Profile Photo

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…

omega-ml (@omega_ml) 's Twitter Profile Photo

#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

Patrick Senti (@productaizery) 's Twitter Profile Photo

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

Just about to publish a #GenAI extension to my MLOps platform <a href="/omega_ml/">omega-ml</a>. 

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
Patrick Senti (@productaizery) 's Twitter Profile Photo

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