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Search results for machine learning helm
helm
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machine-learning
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13 search results found
Nboost
⭐
439
NBoost is a scalable, search-api-boosting platform for deploying transformer models to improve the relevance of search results on different platforms (i.e. Elasticsearch)
Primehub
⭐
367
open-source MLOps platform
Kubernetes Mlops
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284
MLOps tutorial using Python, Docker and Kubernetes.
Kubeflow Labs
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257
👩🔬 Train and Serve TensorFlow Models at Scale with Kubernetes and Kubeflow on Azure
Ml Hub
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198
🧰 Multi-user development platform for machine learning teams. Simple to setup within minutes.
Ml Monitoring
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102
A demo of Prometheus+Grafana for monitoring an ML model served with FastAPI.
Stackn
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36
A minimalistic and pluggable machine learning platform for Kubernetes.
Charts
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33
Helm charts for creating reproducible and maintainable deployments of Polyaxon with Kubernetes.
Hub
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30
A scalable single pane of glass for your ever growing surveillance landscape with dashboards, analytics, notifications, device management, sites and grouping.
Ml Workflow Automation
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20
Python Machine Learning (ML) project that demonstrates the archetypal ML workflow within a Jupyter notebook, with automated model deployment as a RESTful service on Kubernetes.
Ml Cd Starter Kit
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13
Set up cross-cutting services (e.g. CI server, monitoring) for ML projects using kubernetes and helm
H2o Kubernetes
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12
H2O Open Source Kubernetes operator and a command-line tool to ease deployment (and undeployment) of H2O open-source machine learning platform H2O-3 to Kubernetes.
Mlflow
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6
mlflow container setup for docker, docker compose and kubernetes including helm chart
K3ai Plugins
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5
K3ai plugins Repo is the place where we host all the optional capabilites of k3ai. The main goal of the repo is to mantainer k3ai simple and lightweight while adding capabilites in the form of manifests or helm charts.
Kubernetes Operator Roiergasias
⭐
5
'Roiergasias' kubernetes operator is meant to address a fundamental requirement of any data science / machine learning project running their pipelines on Kubernetes - which is to quickly provision a declarative data pipeline (on demand) for their various project needs using simple kubectl commands. Basically, implementing the concept of No Ops. The fundamental principle is to utilise best of docker, kubernetes and programming language features to run a workflow with minimal workflow definition s
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1-13 of 13 search results
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