Ask an OpenShift Admin (Ep 107) Red Hat OpenShift Data Science

Joined by Robert Lundberg from the Red Hat OpenShift Data Science (RHODS) team to discuss the RHODS platform! Check out the stream here: https://www.youtube.com/live/DDvkMj-czFU?feature=share

Transcript

1. Presentation title should not exceed two lines 1 MLOps platform for artificial intelligence / machine learning (AI/ML) use cases Overview of Red Hat OpenShift Data Science Robert Lundberg Senior Architect at OpenShift AI
2. 2 Source: https://proceedings.neurips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Hidden Technical Debt in Machine Learning Systems Model code is one component of a larger system "Only a small fraction of real-world ML systems is composed of the ML code…The required surrounding infrastructure is vast and complex." "Developing and deploying ML systems is relatively fast and cheap, but maintaining them over time is difficult and expensive" Developing and deploying ML is fast and cheap >> configuration data collection data verification machine resource management serving infrastructure monitoring analysis tools process management feature extraction (Adapted from Sculley et al., "Hidden Technical Debt in Machine Learning Systems." NIPS 2015 model code
3. Why containers, Kubernetes and Red Hat OpenShift for Data Science >> 3 https://kubernetes.io/ https://www.redhat.com/en/technologies/cloud-computing/openshift/red-hat-openshift-kubernetes https://aws.amazon.com/blogs/opensource/why-use-docker-containers-for-machine-learning-development/ Why Kubernetes? • Automated rollouts and rollbacks • Self-healing • Service discovery and load balancing • Horizontal scaling • Designed for extensibility Why OpenShift? • Self Service Model • Web UI based Workflows • Metrics and Monitoring • Real-Time, Batch and Streaming Support • Users can Focus on Data Science • Zero Trust Security Model • GPU Support • Cloud and Platform Agnostic Why containers? • Fewer resources • Environment isolation • Quick deployment • Quick startup/shutdown • Encapsulation and portability • Reusability • Reproducible Why containers, K8s, RHOCP for Data Science? Kubernetes ... Red Hat OpenShift Production Ready IMAG E CONTAINE R IMAGE Image registry IMAG E IMAG E IMAG E IMAG E IMAG E [ containers, Kubernetes, Red Hat Openshift ]
4. Upstream code enhanced with operational excellence Open Data Hub: https://opendatahub.io Overview of Red Hat OpenShift Data Science 4 Red Hat OpenShift Data Science Open Data Hub Community driven upstream meta-project demonstrating AI/ML platform on Red Hat OpenShift comprised of open source projects Red Hat OpenShift Data Science: cloud service Subset of Open Data Hub delivered as a cloud service on Red Hat OpenShift Managed services with optional ISV offerings Red Hat OpenShift Data Science: self managed Fast moving software stream mirrors the release frequency and capabilities of the cloud service delivered in a self-managed offering for on-premise OpenShift customers
5. 5 Red Hat OpenShift Data Science Hybrid MLOps platform Model development Conduct exploratory data science in JupyterLab with access to core AI / ML libraries and frameworks including TensorFlow and PyTorch using our notebook images or your own. Collaborate within a common platform to bring IT, data science, and app dev teams together Model serving & monitoring Deploy models across any cloud, fully managed, and self-managed OpenShift footprint and centrally monitor their performance. Lifecycle Management Create repeatable data science pipelines for model training and validation and integrate them with devops pipelines for delivery of models across your enterprise. Increased capabilities / collaboration Create projects and share them across teams. Combine Red Hat components, open source software, and ISV certified software. Now available as fully managed cloud service or traditional software product on-prem or in the cloud!
6. Gather and prepare data Integrate models in app dev Model monitoring and management Develop model 6 Overview of Red Hat OpenShift Data Science Open hybrid cloud platform with self service capabilities Accelerators Cloud infrastructure Red Hat OpenShift Data Science Red Hat OpenShift Service on Amazon Web Services Retrain models ISV managed cloud services Red Hat software and cloud services Red Hat on premise and cloud platform Customer managed ISV software Cloud service and self-managed components model serving Streams data science pipelines model monitoring
7. Demo
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