The Azure MCP Server implements the MCP specification to create a seamless connection between AI agents and Azure services. It allows agents to interact with various Azure services like AI Search, App Configuration, Cosmos DB, and more.
Running GenAI models is easy. Scaling them to thousands of users, not so much. This guide details avenues for scaling AI workloads from proofs of concept to production-ready deployments, covering API integration, on-prem deployment considerations, hardware requirements, and tools like vLLM and Nvidia NIMs.
K8S-native cluster-wide deployment for vLLM. Provides a reference implementation for building an inference stack on top of vLLM, enabling scaling, monitoring, request routing, and KV cache offloading with easy cloud deployment.
vLLM Production Stack provides a reference implementation on how to build an inference stack on top of vLLM, allowing for scalable, monitored, and performant LLM deployments using Kubernetes and Helm.
Solo.io donated Kagent, its open source framework for AI agents in Kubernetes, to the CNCF, and introduced MCP Gateway. They also unveiled automated zero-downtime migration and cost-analysis tools for Ambient Mesh.
This Splunk Lantern article outlines the steps to monitor Gen AI applications with Splunk Observability Cloud, covering setup with OpenTelemetry, NVIDIA GPU metrics, Python instrumentation, and OpenLIT integration to monitor GenAI applications built with technologies like Python, LLMs (OpenAI's GPT-4o, Anthropic's Claude 3.5 Haiku, Meta’s Llama), NVIDIA GPUs, Langchain, and vector databases (Pinecone, Chroma) using Splunk Observability Cloud. It outlines a six-step process:
1. **Access Splunk Observability Cloud:** Sign up for a free trial if needed.
2. **Deploy Splunk Distribution of OpenTelemetry Collector:** Use a Helm chart to install the collector in Kubernetes.
3. **Capture NVIDIA GPU Metrics:** Utilize the NVIDIA GPU Operator and Prometheus receiver in the OpenTelemetry Collector.
4. **Instrument Python Applications:** Use the Splunk Distribution of OpenTelemetry Python agent for automatic instrumentation and enable Always On Profiling.
5. **Enhance with OpenLIT:** Install and initialize OpenLIT to capture detailed trace data, including LLM calls and interactions with vector databases (with options to disable PII capture).
6. **Start Using the Data:** Leverage the collected metrics and traces, including features like Tag Spotlight, to identify and resolve performance issues (example given: OpenAI rate limits).
The article emphasizes OpenTelemetry's role in GenAI observability and highlights how Splunk Observability Cloud facilitates monitoring these complex applications, providing insights into performance, cost, and potential bottlenecks. It also points to resources for help and further information on specific aspects of the process.
1. **Access Splunk Observability Cloud:** Sign up for a free trial if needed.
2. **Deploy Splunk Distribution of OpenTelemetry Collector:** Use a Helm chart to install the collector in Kubernetes.
3. **Capture NVIDIA GPU Metrics:** Utilize the NVIDIA GPU Operator and Prometheus receiver in the OpenTelemetry Collector.
4. **Instrument Python Applications:** Use the Splunk Distribution of OpenTelemetry Python agent for automatic instrumentation and enable Always On Profiling.
5. **Enhance with OpenLIT:** Install and initialize OpenLIT to capture detailed trace data, including LLM calls and interactions with vector databases (with options to disable PII capture).
6. **Start Using the Data:** Leverage the collected metrics and traces, including features like Tag Spotlight, to identify and resolve performance issues (example given: OpenAI rate limits).
The article emphasizes OpenTelemetry's role in GenAI observability and highlights how Splunk Observability Cloud facilitates monitoring these complex applications, providing insights into performance, cost, and potential bottlenecks. It also points to resources for help and further information on specific aspects of the process.
Eran Bibi, co-founder and chief product officer at Firefly, discusses two open-source AI tools, AIaC and K8sGPT, that aim to reduce DevOps friction by automating tasks such as generating IaC code and troubleshooting Kubernetes issues.
- AIaC (AI as Code):
An open source command-line interface (CLI) tool that enables developers to generate IaC (Infrastructure as Code) templates, shell scripts, and more using natural language prompts.
Example: Generating a secure Dockerfile for a Node.js application by describing requirements in natural language.
Benefits: Reduces the need for manual coding and errors, accelerating the development process.
- K8sGPT:
An open source tool developed by Alex Jones within the Cloud Native Computing Foundation (CNCF) sandbox.
Uses AI to analyze and diagnose issues within Kubernetes clusters, providing human-readable explanations and potential fixes.
Example: Diagnosing a Kubernetes pod stuck in a pending state and suggesting corrective actions.
Benefits: Simplifies troubleshooting, reduces the expertise required, and empowers less experienced users to manage clusters effectively.
- AIaC (AI as Code):
An open source command-line interface (CLI) tool that enables developers to generate IaC (Infrastructure as Code) templates, shell scripts, and more using natural language prompts.
Example: Generating a secure Dockerfile for a Node.js application by describing requirements in natural language.
Benefits: Reduces the need for manual coding and errors, accelerating the development process.
- K8sGPT:
An open source tool developed by Alex Jones within the Cloud Native Computing Foundation (CNCF) sandbox.
Uses AI to analyze and diagnose issues within Kubernetes clusters, providing human-readable explanations and potential fixes.
Example: Diagnosing a Kubernetes pod stuck in a pending state and suggesting corrective actions.
Benefits: Simplifies troubleshooting, reduces the expertise required, and empowers less experienced users to manage clusters effectively.
This Splunk Lantern blog post highlights new articles on instrumenting LLMs with Splunk, leveraging Kubernetes for Splunk, and using Splunk Asset and Risk Intelligence.
This article explores the use of LLMs for Kubernetes troubleshooting with k8sgpt, a tool that utilizes OpenAI to analyze Kubernetes clusters, identify issues, and provide explanations.
How to use Kubernetes to manage and streamline AI workflows, leveraging the power of open source tools and the Kubernetes AI Toolchain Operator.
