An #MLOps Repo: https://github.com/mlrun/mlrunMLRun

MLRun is a generic and convenient mechanism for #data scientists and software developers to build, run, and monitor #machinelearning (ML) tasks and pipelines on a scalable cluster while automatically tracking executed code, metadata, inputs, and outputs. MLRun integrates with the Nuclio (https://github.com/nuclio/nuclio) stateful #serverless project and with #Kubeflow Pipelines (https://github.com/kubeflow/kubeflow).

MLRun is architected for scale with Kubernetes (#k8s) in mind from the get-go. This video describes the productivity boost data science professionals and teams can enjoy by employing traditional software engineering concepts of the re-usability of functions within automated and recurring workflows. CDOs, CIOs and Chief / Heads of Data Science can increase delivery and collaboration between team members for agile / scrum methodologies. Internal and external function marketplaces/stores are their advantages are detailed.MLRun runs as an (optional) managed service within the end-to-end real-time data science platform: iguazio.com. (https://www.iguazio.com/) The enterprise-grade solution can be installed as a: 1. Managed service (SaaS - Iguazio (https://www.iguazio.com/) manages the infrastructure), 2. (Hybrid) Cloud agnostic PaaS (customer VPC) 3. On-Premise or Barebone/Metal - including Edge AI / Analytics Customers include NetApp, Quadient, Payoneer (and many more).