Exploring Modern AI Trends and Kubernetes-Based AI Infrastructure
Description
Build a solid understanding of the current landscape of Artificial Intelligence and how modern cloud-native technologies—especially Kubernetes—support AI workloads.
Goals
Use Gemini Learning Mode to guide the exploration, surface relevant concepts, and structure the learning journey:
- Gain insight into the latest AI trends, tools, and architectural concepts.
- Understand how Kubernetes and related cloud-native technologies are used in the AI ecosystem (model training, deployment, orchestration, MLOps).
Resources
Red Hat AI Topic Articles
- https://www.redhat.com/en/topics/ai
Kubeflow Documentation
- https://www.kubeflow.org/docs/
Q4 2025 CNCF Technology Landscape Radar report:
- https://www.cncf.io/announcements/2025/11/11/cncf-and-slashdata-report-finds-leading-ai-tools-gaining-adoption-in-cloud-native-ecosystems/
- https://www.cncf.io/wp-content/uploads/2025/11/cncfreporttechradar_111025a.pdf
Agent-to-Agent (A2A) Protocol
- https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
Looking for hackers with the skills:
This project is part of:
Hack Week 25
Activity
Comments
-
about 1 hour ago by jluo | Reply
A great summary from Gemini:
This is a rapidly expanding ecosystem. To keep it organized, I will break this list down by Lifecycle Stage (Training vs. Serving) and Infrastructure Layer (Compute vs. Data).
1. AI Platforms & Orchestration (The "Command Center")
These tools manage the end-to-end lifecycle, gluing everything else together.
- Kubeflow: The "Grandfather" of AI on K8s. It’s a massive suite including:
- Kubeflow Pipelines: For building repeatable workflows (Data -> Train -> Deploy).
- Kubeflow Notebooks: Spawns Jupyter servers as Pods for data scientists.
- Katib: Automated hyperparameter tuning (finding the best learning rate).
- Ray (KubeRay): The top challenger to Kubeflow. It allows you to write Python code that scales across a cluster instantly. It is excellent for both distributed training and serving.
- ZenML: An MLOps framework that sits above the infrastructure, letting you define pipelines in code that can run on Kubeflow, Ray, or simple Kubernetes batches.
2. Training & Scheduling (The "Heavy Lifters")
Standard Kubernetes scheduling (FIFO) is bad for AI training. These tools fix that.
- Volcano: A batch scheduler. It ensures "Gang Scheduling"—meaning if a job needs 50 GPUs but only 49 are available, it waits. (Standard K8s would start 49 and let them sit idle, wasting money).
- Kueue: A newer, lighter alternative to Volcano managed by the K8s specialized interest group. It manages "Job Queues" natively.
- Training Operator: A unified K8s operator that lets you run
PyTorchJob,TFJob(TensorFlow), andXGBoostJobas native K8s objects.
3. Inference & Serving (The "Waiter")
Once a model is trained, these tools serve it to users.
- KServe: The industry standard. It handles "Scale-to-Zero" (via KEDA), canary rollouts, and provides a unified API for TensorFlow, PyTorch, and ONNX models.
- vLLM: The current king of LLM serving. It is highly optimized for GPU memory (PagedAttention) and is often run inside KServe or as a standalone Deployment.
- BentoML / Yatai: A developer-friendly framework. You package your model as a "Bento" (standard format), and Yatai orchestrates the deployment on K8s.
- Seldon Core: An enterprise-grade alternative to KServe with advanced features for compliance, audit trails, and complex inference graphs.
4. Agentic & LLM Ops (The "New Wave")
Tools specifically for the 2025 era of Autonomous Agents.
- LangFlow / Flowise: Low-code "drag-and-drop" UI tools for building LLM chains. They can be deployed on K8s via Helm charts to run agent backends.
- kagent / Agent Sandbox: Emerging tools (often cloud-specific or experimental) that provide secure, isolated environments (using gVisor or microVMs) for agents to execute code safely.
- Ollama: While often used locally, it is increasingly deployed on K8s (via Helm) as a lightweight way to serve open-source models like Llama 3 or Mistral inside a cluster.
5. Data & Memory (The "Brain")
- Vector Databases (with K8s Operators):
- Milvus: A popular open-source vector DB built natively for K8s scalability.
- Weaviate: Another strong option with a solid K8s operator.
- Qdrant: Written in Rust, very fast, and easy to deploy on K8s.
- Feature Stores:
- Feast: The open-source standard for serving features (e.g., "User's last 5 clicks") to models in real-time.
6. Observability & Cost (The "Watchtower")
- Prometheus & Grafana: The standard for metrics (GPU temperature, Request Latency).
- DCGM Exporter: The specific NVIDIA tool that pulls GPU metrics (utilization, memory) so Prometheus can see them.
- KEDA: The autoscaler (discussed previously) that scales pods based on event queues.
- Karpenter: The Node autoscaler. If KEDA asks for more pods, Karpenter instantly buys more EC2/VM nodes from the cloud provider to fit them.
- OpenCost / Kubecost: Tools to track exactly how much money your AI team is spending on GPUs per namespace.
- Kubeflow: The "Grandfather" of AI on K8s. It’s a massive suite including:
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Description
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Goals
By the end of Hack Week, the project should:
Produce a fully functional ML pipeline running on Kubernetes with:
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Enable continuous improvement by making the system modular and extensible (e.g., additional models, metrics, autoscaling, or drift detection can be added later).
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Resources