Express YourSelf !

Examples: 

Istio, Kong, Envoy , Gluee , Trafeik, kgateway, Contour, NGINX, Kong Gateway and many more as per https://gateway-api.sigs.k8s.io/implementations/#gateway-controller-implementation-status

Protocols: gRPC, HTTP/2, and WebSockets

The structure of a Kubernetes Custom Resource Definition (CRD) or manifest file is referred to as an API. This is because it refers to the structure of the API in the Kubernetes control plane

Migration from ingress https://gateway-api.sigs.k8s.io/guides/migrating-from-ingress/#migrating-from-ingress

extension points

ingress has 2 extension points

1. annotations

2. resource end point

primary extension points in GW API:

1. External references

1.1 HTTP Route Filter

1.2 Backend Object Reference

1.3 Secret Object Reference

Here GW API reference 'external reference'

2. Custom implementations

e.g. The RegularExpression type of the 'HTTP Path Match'

3. Policies

A "Policy Attachment" is a specific type of metaresource

Here, Policy reference 'GW API'

• GW API is not API GW
• GAMMA (Gateway API for Mesh Management and Administration) initiative
• A 'waypoint proxy' is a proxy server deployed inside the mesh for E-W traffic. It can be (1) per namespace level destination services (2) few service(s) within a namespace as destination (3) destination services from multiple namespaces. 

- It is at cluster level. so no namespace

- Annotations at GatewayClassfor vendor specific

- It defines controller capabilities

2. Gateway

- Each Gateway defines one or more listeners, which are the ingress points to the cluster

- You can control which services can be connected to this listener (allowedRoutes) by way of their namespace — this defaults to the same namespace as the Gateway 

- Advanced featues like 

-- request mirroring, 

-- direct response injection, 

-- and fine-grained traffic metrics

-- Traffic spilt

- In Istio APIs, a Gateway configures an existing gateway Deployment/Service that has been deployed. In the Gateway APIs, the Gateway resource both configures and deploys a gateway

- one can attach HPA and PodDisuptionBudget to gateway deployment. 

3. HTTP Route: 

- any combinations of hostname, path, header values, HTTP method and query parameters.

• Paths (e.g., /headers, /status/*)
• Headers (e.g., User-Agent: Mobile)
• Query Parameters (e.g., ?version=beta)
• Methods (e.g., GET, POST)

- hostname (optional) at HTTP route shall match with hostname at Gateway->Listener->hostname

- A definition of the Gateway to use (in ParentRefs), is referenced by name and namespace

- The backendRefs that defines the service to route the request to for this match

- advanced pattern matching and filtering on arbitrary headers as well as paths.

1. RequestRedirect : E.g. Redirect HTTP traffic to HTTPS

2. URLRewrite

3. <Request|Response>HeaderModifier

4. RequestMirror

5. CORS

6. ExtensionRef for custom filter. E.g. DirectResponse

    filters:

    - type: ExtensionRef

      extensionRef:

       name: direct-response

       group: gateway.kgateway.dev

       kind: DirectResponse

- In the Istio VirtualService, all protocols are configured within a single resource. In the Gateway APIs, each protocol type has its own resource, such as HTTPRoute and TCPRoute.

- Traffic splitting is done by specifying multiple backendRef, with weight

- timeout, retry, sessionPersistence Session persistence, (= sticky sessions or strong session affinity), ensures that a client's requests are consistently routed to the same backend instance for the duration of a session. based on cookie or a header

- Route and Gateway can be in different namespace. If Gateway is defined with

    allowedRoutes:

      namespaces:

        from: Same

then Route and Gateway shall be in same namespace. We can have group of namespaces with label selector and specify those namespace using label at Gatway resource. 

    allowedRoutes:

      namespaces:

        from: Selector

        selector:

          matchLabels:

            self-serve-ingress: "true"

* 4. TLS Route

5. GRPCRoute

* 6. TCPRoute

* not v1, GA

Details: https://gateway-api.sigs.k8s.io/reference/spec/

If you are using a service mesh, it would be highly desirable to use the same API resources to configure both ingress traffic routing and internal traffic, similar to the way Istio uses VirtualService to configure route rules for both. Fortunately, the Kubernetes Gateway API is working to add this support. Although not as mature as the Gateway API for ingress traffic, an effort known as the Gateway API for Mesh Management and Administration (GAMMA) initiative is underway to make this a reality and Istio intends to make Gateway API the default API for all of its traffic management in the future.

https://gateway-api.sigs.k8s.io/mesh/

Gateway controller is for North South traffic. mesh controller is for East West traffic

7. ReferenceGrant: for cross-namespace reference. 

8. Inference Extension

K8s offers following mechanisms to optimize GPU usage, 

- time slicing, 

- Multi-Instance GPU (MIG) partitioning, 

- virtual GPUs, and 

- NVIDIA MPS 

for concurrent processing.

Effective GPU utilization means

- hardware allocation; 

- how inference requests are routed across model-serving instances. 

- how inference requests are load-balanced across model-serving instances. 

Simple load-balancing strategies often fall short in handling AI workloads effectively, leading to suboptimal GPU usage and increased latency.

Inference requests V/s traditional web traffic

- It often takes much longer to process, sometimes several seconds (or even minutes!) rather than milliseconds, 

- It has significantly larger payloads (ie, with RAG, multi-turn chats, etc). So a single request can consume an entire GPU, So making scheduling decisions far more impactful than those for standard API workloads. So, these requests need to queue up while others are being processed.

AI Models are stateful

- They maintain in-memory caches, such as KV storage for prompt tokens,

- They load fine tuned adapters like LoRA to customize response for specific user/organisation. 

So routing decisions are based on

- current state (in-memory caches, adapters)

- available memory, and 

- request queue depth.

So Inference aware routing through

8.1 Inference Model

- maps user facing model name to backend model

- traffic splitting between fine-tuned adapaters

- Priority based on real time interaction OR best-effort batch job

8.2 Inference Pool

- It is for platform operators managing model-serving infrastructure.

- a group of model-serving instances 

- specialized backend service for AI workloads.

- It manages

-- inference-aware endpoint selection, 

-- intelligent routing decisions based on real-time metrics such as 

--- request queue depth and 

--- GPU memory availability.

* Inference Pool is mapped with HTTP Route->backendRefs

* Inference Model has poolRef to link with Inference Pool

* Inference Pool has extensionRef (EPP = Endpoint picker) . If name for Inference Pool is xyz then extensionRef is "xyz-endpoint-picker" It is similar to K8s service, as it also has selector and target port

9. DirectResponse

10. Backend

For external endpoint

Ref: https://tetrate.io/blog/kubernetes-envoy-gateway-extensions

LTM Large Teleco Model : SoftBank is pioneer. Here is WhitePaper by GSMA https://www.gsma.com/get-involved/gsma-foundry/gsma_resources/white-paper-large-telecom-models/

Llama Nemotron Reasoning Model. Open source by NVIDIA on HF

https://www.nvidia.com/en-in/ai-data-science/foundation-models/nemotron/

https://arxiv.org/pdf/2505.00949

AI Factory is a specialized, integrated infrastructure designed to manage the entire AI lifecycle, from data ingestion to model training and deployment for real-time inference

AI Grid is a network of small, highly specialized AI communities. The members of AI Grid share their research work within these communities, initiate collaborations and establish fruitful connections for the future. https://lightning.ai/

https://ai-ran.org/

Building Blocks of the NVIDIA AI Aerial Platform: 

1. NVIDIA Aerial CUDA-Accelerated RAN

2. NVIDIA Aerial AI Radio Frameworks

3. NVIDIA Aerial Omniverse Digital Twin

Reference

NVIDIA GTC25: Telecom Special Address

 8 Day Live Online Workshop

AI Bootcamp for Students

Make Your Child Future-Ready with AI

by Timesof Inida

https://www.notion.com/product Documentation

https://www.todoist.com/ To Do List

https://gamma.app/ For presentation 

https://openai.com/index/sora/ Cinematic Video 

https://www.midjourney.com/home Art Grade Visuals for story telling

https://ideogram.ai/t/explore Typography to image. Communicate in style

https://lovable.dev/ No code web apps

https://n8n.io/ Workflow automation tools

Few more tools

TachyonGPT accelerate the project planning process, potentially saving weeks of effort. This powerful AI assistant allows you to create a complex backlog structure for your project in very little time. Tachyon GPT gives you the power to improve existing work items or generate new work items based on brief titles or descriptions. https://marketplace.visualstudio.com/items?itemName=Neudesic.TachyonGPT

windserf editor and cascade. Agentic code IDE

Reference: https://economictimes.indiatimes.com/masterclass/ai-for-students

https://www.msn.com/en-in/money/news/chatgpt-to-google-gemini-top-5-ai-tools-to-enhance-productivity-mostly-free/ar-AA1GRlt1

New Language Learning

Want to learn a new language this summer? Explore these expert-led platforms

Mobile app https://youtu.be/jyffkeM9GB0

Regional LLM

Sarvam AI launches Bulbul-v2, its voice model with support for 11 Indian languages

https://asr.iitm.ac.in/models/ 

BharatGen  https://bharatgen.tech/  Bharatgen: First Indigenous Language Ai Model Launched In India News In Hindi - Amar Ujala Hindi News Live - Bharatgen:भारत में लॉन्च हुआ पहला स्वदेशी भाषा Ai मॉडल, 22 भाषाओं में करेगा अनुवाद; दूर होंगी संवाद चुनौतियां and Google to collaborate with IIT Bombay’s BharatGen to build indigenous Indic language model

https://aikosha.indiaai.gov.in/home It looks like huggingface website for India

Models https://aikosha.indiaai.gov.in/home/models/all 

Datasets https://aikosha.indiaai.gov.in/home/datasets/all

https://aikosha.indiaai.gov.in/home/resources?from=resource-detail having some PDF books 

https://aikosha.indiaai.gov.in/home/toolkit having a list of popular AI related tools.