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Open Source Developer Experience, Platform Engi...

Open Source Developer Experience, Platform Engineering and AI-infused Apps - DevTalks Romania

Kevin Dubois

May 30, 2024
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  1. Open Source Developer Experience, Platform Engineering and AI Infused Apps

    Kevin Dubois Principal Developer Advocate @kevindubois
  2. AI

  3. Kevin Dubois ★ Principal Developer Advocate at Red Hat ★

    Based in Belgium 󰎐 ★ 🗣 Speak English, Dutch, French, Italian ★ Open Source Contributor (Quarkus, Camel, Knative, ..) @[email protected] youtube.com/@thekevindubois linkedin.com/in/kevindubois github.com/kdubois @kevindubois.com
  4. 80% Rise of Generative AI of Enterprises will have deployed

    Generative AI-Enabled Applications by 2026 Source: Gartner
  5. App developer IT operations Data engineer Data scientists ML engineer

    Business leadership AI as a team initiative
  6. Set goals App developer IT operations Data engineer Data scientists

    ML engineer Gather and prepare data Develop model Integrate models in app dev Model monitoring & management Retrain models Business leadership AI as a team initiative
  7. Data storage Data lake Data exploration Data preparation Stream processing

    ML notebooks ML libraries Model lifecycle CI/CD Monitor / alerts Model visualization Model drift Hybrid, multi cloud platform with self service capabilities Compute acceleration Infrastructure Gather and prepare data Deploy models in an application Model monitoring and management Physical Virtual Private cloud Public cloud Edge Develop model Team Deliverables Data engineer Data scientists App developer IT operations
  8. 16

  9. Open source fuels rapid innovation 18 More than 1,000,000 projects

    Cloud Mobile Storage Networking Platforms Infrastructure
  10. 21 Process scheduling & hardware acceleration Containerization & container orchestration

    Operating containers at scale Software-defined storage Data visualization, labeling, processing Automated software delivery Integration Experimentation & model lifecycle Languages & development tools AI dependencies Libraries and frameworks Machine learning libraries
  11. 22 Process scheduling & hardware acceleration Containerization & container orchestration

    Operating containers at scale Software-defined storage Data visualization, labeling, processing Automated software delivery Integration Experimentation & model lifecycle Languages & development tools AI dependencies Libraries and frameworks Languages & development tools Machine learning libraries
  12. 23 Process scheduling & hardware acceleration Containerization & container orchestration

    Operating containers at scale Software-defined storage Data visualization, labeling, processing Automated software delivery Integration Experimentation & model lifecycle Languages & development tools AI dependencies Libraries and frameworks Data visualization, labeling, processing Experimentation & model lifecycle Languages & development tools Machine learning libraries
  13. 24 Process scheduling & hardware acceleration Containerization & container orchestration

    Operating containers at scale Software-defined storage Data visualization, labeling, processing Automated software delivery Integration Experimentation & model lifecycle Languages & development tools AI dependencies Libraries and frameworks Process scheduling & hardware acceleration Data visualization, labeling, processing Experimentation & model lifecycle Languages & development tools Machine learning libraries
  14. 25 Process scheduling & hardware acceleration Containerization & container orchestration

    Operating containers at scale Software-defined storage Data visualization, labeling, processing Automated software delivery Integration Experimentation & model lifecycle Languages & development tools AI dependencies Libraries and frameworks Process scheduling & hardware acceleration Containerization & container orchestration Data visualization, labeling, processing Experimentation & model lifecycle Languages & development tools Machine learning libraries
  15. 26 Process scheduling & hardware acceleration Containerization & container orchestration

    Operating containers at scale Software-defined storage Data visualization, labeling, processing Automated software delivery Integration Experimentation & model lifecycle Languages & development tools AI dependencies Libraries and frameworks Process scheduling & hardware acceleration Containerization & container orchestration Operating containers at scale Automated software delivery Data visualization, labeling, processing Experimentation & model lifecycle Languages & development tools Machine learning libraries
  16. 27 Process scheduling & hardware acceleration Containerization & container orchestration

    Operating containers at scale Automated software delivery Software-defined storage Integration Data visualization, labeling, processing Experimentation & model lifecycle Languages & development tools Machine learning libraries AI dependencies
  17. Process scheduling & hardware acceleration Containerization & container orchestration Operating

    containers at scale Automated software delivery Software-defined storage Integration Data visualization, labeling, processing Experimentation & model lifecycle Languages & development tools Machine learning libraries AI & MLOps Platform Application Platform AI dependencies
  18. Process scheduling & hardware acceleration Containerization & container orchestration Operating

    containers at scale Automated software delivery Software-defined storage Integration Data visualization, labeling, processing Experimentation & model lifecycle Languages & development tools Machine learning libraries AI & MLOps Platform Application Platform AI dependencies Modernize & Accelerate app development
  19. 30

  20. 33 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. 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. Available as • managed cloud service • traditional software product on-site or in the cloud! Hybrid MLOps platform Collaborate within a common platform to bring IT, data science, and app dev teams together OpenDataHub.io
  21. MLOps incorporates DevOps and GitOps to improve the lifecycle management

    of the ML application DEVELOP ML CODE BUILD TEST SERVE MONITOR DRIFT/OUTLIER DETECTION Cross Functional Collaboration Automation Repeatability Security Git as Single Source of Truth Observability TRAIN VALIDATE DEVELOP APP CODE Enter MLOps 36 [1] Reference and things to read: https://cloud.redhat.com/blog/enterprise-mlops-reference-design
  22. 37 Sensors and IoT Devices Logs, Files, media cameras 4

    ML Inferencing AI-powered Intelligent Apps ML Inferencing ML models monitoring & management Data ingestion & streaming Data repository 1 Edge / Near Edge Data Acquisition 3 App Dev & Delivery Integrate ML models in app dev processes Pipelines and GitOps 2 Data Preparation, Modeling Data Analytics & ML Modeling Data repository Public clouds On-prem data center Infusing AI into applications
  23. Model to Prod with MLOps Kserve ModelMesh • Model serving

    framework with simplified deployment, auto-scaling, and resource optimization • Supports variety of ML frameworks like TF, PyTorch, etc. Kubeflow Pipelines • Machine Learning lifecycle automation, with model training, evaluation, and deployment • Reusable components for pipeline creation & scaling Backstage • Platform for building IDPs for streamlining developer workflows • Highly customizable, with extensive plugin support and project scaffolding capabilities
  24. Model to Prod with MLOps Model Fine-tuning Model Creation/Training Model

    Serving Data Scientist Flow Model Testing Monitoring/Analysis Application Deployment API Inferencing App Scaffolding Developer Flow Monitoring/Manag ement
  25. 76% of organizations say the cognitive load is so high

    that it is a source of low productivity. Gartner predicts 75% of companies will establish platform teams for application delivery. Source: Salesforce Source: Gartner
  26. Run LLMs locally and build AI applications podman-desktop.io Download now

    at: Supported platforms: From getting started with AI, to experimenting with models and prompts, Podman AI Lab enables you to bring AI into your applications without depending on infrastructure beyond your laptop. Podman AI Lab
  27. Start your OpenShift experience for free in four simple steps

    Recap & Next steps Developer Sandbox for OpenShift OpenShift AI Sandbox Start your OpenShift AI experience for free Sign up at developers.redhat.com Find out more about Red Hat’s project and products, and what it offers developers Learn more about OpenShift AI