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

1. Open Source Developer Experience, Platform Engineering and AI Infused Apps Kevin Dubois Principal Developer Advocate @kevindubois
2. AI
3. Open Source Developer Experience, Platform Engineering and AI Infused Applications
4. Open Source
5. Developer Experience
6. Platform Engineering
7. AI Infused Applications
8. Kevin Dubois ★ Principal Developer Advocate at Red Hat ★ Based in Belgium 󰎐 ★ 🗣 Speak English, Dutch, French, Italian ★ Open Source Contributor (Quarkus, Camel, Knative, ..) @kevindubois@mastodon.social youtube.com/@thekevindubois linkedin.com/in/kevindubois github.com/kdubois @kevindubois.com
9. open source cloud computing and automation artificial intelligence and machine learning
10. 80% Rise of Generative AI of Enterprises will have deployed Generative AI-Enabled Applications by 2026 Source: Gartner
11. AI enabled Apps: • First, create the intelligence • Second, Connect to your App
12. Left / Right of the model
13. App developer IT operations Data engineer Data scientists ML engineer Business leadership AI as a team initiative
14. 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
15. 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
17. AI open source &
18. Open source fuels rapid innovation 18 More than 1,000,000 projects Cloud Mobile Storage Networking Platforms Infrastructure
19. Open Source Models Explosion
20. Open Source AI Platforms
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
31. Productivity, stability, security
32. bringing AI/ML community projects to the enterprise Community Project Product Upstream Projects
33. 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
35. MLOps: DevOps to ML 35
36. 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
37. 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
38. 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
39. 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
40. Developer Portals to relieve cognitive load
41. 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
44. Local development with AI
45. 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
47. https://github.com/kdubois/quarkus-langchain4j-podman-ai-instructlab
48. 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
49. Thank you! @kevindubois@mastodon.social youtube.com/@thekevindubois linkedin.com/in/kevindubois github.com/kdubois @kevindubois.com