Found 98 repositories(showing 30)
SeldonIO
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
SeldonIO
An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
AlexIoannides
MLOps tutorial using Python, Docker and Kubernetes.
kubeflow
Example for end-to-end machine learning on Kubernetes using Kubeflow and Seldon Core
CognonicLabs
:snowflake: :whale: Awesome tools and libs for AI, Deep Learning, Machine Learning, Computer Vision, Data Science, Data Analytics and Cognitive Computing that are baked in the oven to be Native on Kubernetes and Docker with Python, R, Scala, Java, C#, Go, Julia, C++ etc
adriangonz
End to End example integrating MLFlow and Seldon Core
data-max-hq
Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.
siftly-ai
Tool to take your ML model from local to production with one-line of code.
hypnosapos
CartPole game by Reinforcement Learning, a journey from training to inference
saeid93
Examples of inference pipelines implemented using https://github.com/SeldonIO/seldon-core
SeldonIO
Seldon Core Operator for Kubernetes
SeldonIO
Seldon Core Cloud Launcher
yinondn
No description available
SeldonIO
No description available
canonical
Seldon Core Operator
DARK-art108
Serving NLP Models using Seldon Core on Kubernetes
SeldonIO
Seldon Core GCP Marketplace
SeldonIO
Helper function to use JPMML with Seldon-Core
No description available
SeldonIO
Wrap java code for use with seldon-core
dileep-gadiraju
try-seldon-core https://docs.seldon.io/projects/seldon-core/en/latest/index.html
dudeperf3ct
No description available
data-max-hq
Serve contanerized machine learning models in microservice architecture with seldon-core or Tensorflow Serving
sbakiu
In this repo it is show how to build and deploy a simple pipeline using Kubernetes, Kubeflow pipelines and seldon-core.
dudeperf3ct
No description available
cognonic
There are many reasons that the natural inclination to look at the cloud for execution of Kubernetes data science analytics workloads may not be the best first choice for some organizations but CNCF still shows the way forward towards structuring both infrastructure and applications to embrace on-premise environments that enable either eventual or simultaneous scale out to cloud based services. This is an important Cognonic focus topic and reference documentation.
SeldonIO
Seldon Core AWS Marketplace Helm Charts
nunofernandes-plight
No description available
Patrick844
stroke prediction with seldon core
akiyamasho
Diffusion HuggingFace pipelines with Seldon Core