Machine Learning Toolkit for Kubernetes
A guideline for building practical production-level deep learning systems to be deployed in real world applications.
PipelineAI
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Standardized Serverless ML Inference Platform on Kubernetes
Machine Learning Pipelines for Kubeflow
Elyra extends JupyterLab with an AI centric approach.
Distributed ML Training and Fine-Tuning on Kubernetes
Automated Machine Learning on Kubernetes
Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
DoEKS is a tool to build, deploy and scale Data & ML Platforms on Amazon EKS
Kubeflow’s superfood for Data Scientists
Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
Distributed Machine Learning Patterns from Manning Publications by Yuan Tang https://bit.ly/2RKv8Zo
Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...
deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow†, and MLflow† into a complete platform.
👩🔬 Train and Serve TensorFlow Models at Scale with Kubernetes and Kubeflow on Azure
Helmut Hoffer von Ankershoffen experimenting with arm64 based NVIDIA Jetson (Nano and AGX Xavier) edge devices running Kubernetes (K8s) for machine learning (ML) including Jupyter Notebooks, TensorFlow Training and TensorFlow Serving using CUDA for smart IoT.
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