Reproducible Data Science at Scale!

open source platform for simplifying machine learning deployment

❄️ 🐳 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

Python client for Pachyderm

A Python client library for Pachyderm

Pachyderm gRPC client with Scala niceties

An example Pachyderm ML pipeline using Nervana Neon

☀️ Unlocking Fusion with Containers and Kubernetes

Deploy pachyderm using terraform

Realizing End to End Reproducible Machine Learning on Kubernetes

Using pachyderm as pipeline engine to serve "taxi chicago" machine learning models

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

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.

Pachelm [Pla - chelm] (Pachyderm + helm) helm style + laravel migration & seeds style framework to manage pipeline deployment as a package of migrations.

❄️ 🐳 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.

Playing with pachyderm, using haskell

Metabolomics data analysis using Pachyderm

Pachyderm testing

Pachyderm + Elasticsearch

Data Lineage with End-to-End Pipelines on Kubernetes