Welcome to the AI Jungle! Now What? Kevin Dubois (@kevindubois) Senior Principal Developer Advocate, Red Hat

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We’ve been here before…

Operating Systems & App Servers cloud computing and automation artificial intelligence and machine learning

Open Source

7 The Journey of Adopting Generative AI Find LLMs Try prompts Experiment with your data Connect to data source Model serving Exception handling Limited fine tuning Retrieval-Augmented Generation (RAG) Endpoints Evaluate flows Benchmarking Monitoring Integrate with apps Chaining Building & Refining Ideation & Prototyping Operationalizing

8 The Journey of Adopting Generative AI Find LLMs Try prompts Experiment with your data Benchmarking Ideation & Prototyping

“Open Source” Model Explosion

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Open source refers to software whose source code is made publicly available for anyone to view, modify, and distribute. “

Open source refers to software whose source code is made publicly available for anyone to view, modify, and distribute. “

an open-source AI system can be used for any purpose without the need to secure permission, and researchers should be able to inspect its components and study how the system works. It should also be possible to modify the system for any purpose—including to change its output—and to share it with others to use, with or without modifications, for any purpose. In addition, the standard attempts to define a level of transparency for a given model’s training data, source code, and weights. “ https://www.technologyreview.com/2024/08/22/1097224/we-finally-have-a-definition-for-open-source-ai/ Open Source Initiative (OSI):

The project enables community contributors to add additional "skills" or "knowledge" to a particular model. InstructLab's model-agnostic technology gives model upstreams with sufficient infrastructure resources the ability to create regular builds of their open source licensed models not by rebuilding and retraining the entire model but by composing new skills into it.

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16 The Journey of Adopting Generative AI Find LLMs Try prompts Experiment with your data Connect to data source Model serving Exception handling Limited fine tuning Retrieval-Augmented Generation (RAG) Endpoints Evaluate flows Benchmarking Monitoring Integrate with apps Chaining Building & Refining Ideation & Prototyping Operationalizing

17 The Journey of Adopting Generative AI Connect to data source Model serving Exception handling Limited fine tuning Retrieval-Augmented Generation (RAG) Endpoints Evaluate flows Monitoring Integrate with apps Chaining Building & Refining Operationalizing

AI open source &

App developer IT operations Data engineer Data scientists ML engineer Business leadership AI is a team initiative

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 is a team initiative

Open Source AI Platforms

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

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 Machine learning libraries

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 Languages & development tools Machine learning libraries

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 Data visualization, labeling, processing Experimentation & model lifecycle Languages & development tools Machine learning libraries

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 Data visualization, labeling, processing Experimentation & model lifecycle Languages & development tools Machine learning libraries

27 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

28 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

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 dependencies

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

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

32

Productivity, stability, security

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AI/ML + Kubernetes operators

36 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

Meet RH Teddy

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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 MLOps 39 [1] Reference and things to read: https://cloud.redhat.com/blog/enterprise-mlops-reference-design

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

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

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

Developer Portals to relieve cognitive load

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https://github.com/kdubois/quarkus-langchain4j-podman-ai-instructlab

Run LLMs locally and build AI applications podman-desktop.io 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

Recap & Next steps The current AI landscape is growing FAST Let’s democratize AI We can re-use what we already know: ● Embrace open source ● Embrace platform engineering for AI: Containers, CI/CD, GitOps, Developer Portals Traditional engineering can (inf)use AI, and we’re only just at the beginning

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

Free Developer e-Books & Tutorials! developers.redhat.com/eventtutorials

Thank you! @kevindubois@mastodon.social youtube.com/@thekevindubois linkedin.com/in/kevindubois github.com/kdubois slides opendatahub.io instructlab.ai podman-desktop.io docs.quarkiverse.io/quarkus-langchain4j