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MLOps tutorial using Python, Docker and Kubernetes.
Example for end-to-end machine learning on Kubernetes using Kubeflow and Seldon Core
: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
Deploy A/B testing infrastructure in a containerized microservice architecture for Machine Learning applications.
CartPole game by Reinforcement Learning, a journey from training to inference
Tool to take your ML model from local to production with one-line of code.
In this repo it is show how to build and deploy a simple pipeline using Kubernetes, Kubeflow pipelines and seldon-core.
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.
:chart: Cognitive Services, ML, Machine Learning, Data Ingestion, Azure, Docker, Container, R, Python, C#, Java, Hadoop, Spark etc. This is also a Cognonic reference doc.
Serve contanerized machine learning models in microservice architecture with seldon-core or Tensorflow Serving
: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++, Cognonic, etc.
Simple docker container to get dcos with k8s and kubeflow
End to end ML production pipeline from training to deployment using MLflow and Seldon Core
MLOps for Reddit Text Classification using Kubeflow pipelines and related orchestration components
A deployment using Seldon's open source MLServer
A SDaaS for building Big Data, Machine Learning and Serverless applications based on cost prediction driven development