An introduction to machine learning model deployment operations (MLOps) using Python, Docker, Kubernetes and Seldon-Core.

Example for end-to-end machine learning on Kubernetes using Kubeflow and Seldon Core

❄️ 🐳 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

CartPole game by Reinforcement Learning, a journey from training to inference

❄️ 🐳 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.

💹 Cognitive Services, ML, Machine Learning, Data Ingestion, Azure, Docker, Container, R, Python, C#, Java, Hadoop, Spark etc. This is also a Cognonic reference doc.

Simple docker container to get dcos with k8s and kubeflow

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.

A SDaaS for building Big Data, Machine Learning and Serverless applications based on cost prediction driven development