NVIDIA/nim-deploy
Jan 2025 – May 2025
3 Months active
Languages Used
PythonShellYAMLMarkdownBashJSONJupyter Notebookbash
Technical Skills
API IntegrationAzureAzure Machine LearningCloud ComputingContainerizationDocker
Alex Zeltov
PROFILE
Alex Zeltov
Alex Zeltov developed end-to-end machine learning deployment workflows for the NVIDIA/nim-deploy repository, focusing on secure, reproducible solutions for Azure environments. He built air-gapped Llama 3.1 70B deployments on Azure Machine Learning using Python and shell scripting, implementing secure API key handling and offline model caching. Alex also authored a comprehensive RAG AKS Deployment Workshop Guide, detailing infrastructure automation and integration of NVIDIA NIMs and NeMo Retriever on Azure Kubernetes Service. In addition, he delivered a persistent volume-backed Llama 3.1-8B deployment with GPU acceleration, refining Helm-based scripts and documentation. His work demonstrated depth in cloud, containerization, and MLOps practices.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
13Total
Bugs
1
Commits
13
Features
3
Lines of code
4,332
Activity Months3
Work History
May 2025
10 Commits • 1 Features
May 1, 2025May 2025 — NVIDIA/nim-deploy: Delivered an end-to-end Llama 3.1-8B Instruct NIM deployment workflow on Azure Kubernetes Service (AKS) with persistent volume storage (Azure Files via PVC) and GPU acceleration (NVIDIA GPU Operator). Produced a repeatable workshop, notebooks, deployment code, and documentation updates to demonstrate PVC-based workflows and CLI-based verification. Implemented PVC-backed model weights storage using Azure Blob Filestore and added an AKS PVC Nim demo. Addressed code review feedback to refine deployment scripts and CLI examples.
10 Commits • 1 Features
May 1, 2025May 2025 — NVIDIA/nim-deploy: Delivered an end-to-end Llama 3.1-8B Instruct NIM deployment workflow on Azure Kubernetes Service (AKS) with persistent volume storage (Azure Files via PVC) and GPU acceleration (NVIDIA GPU Operator). Produced a repeatable workshop, notebooks, deployment code, and documentation updates to demonstrate PVC-based workflows and CLI-based verification. Implemented PVC-backed model weights storage using Azure Blob Filestore and added an AKS PVC Nim demo. Addressed code review feedback to refine deployment scripts and CLI examples.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 (2025-04): Delivered the RAG AKS Deployment Workshop Guide for NVIDIA/nim-deploy. This end-to-end guide documents deploying a Retrieval Augmented Generation stack on Azure Kubernetes Service using NVIDIA NIMs and NeMo Retriever, including LLM, embedding, and reranking microservices with Milvus as the vector store. It covers infrastructure deployment steps, NVIDIA software integration, and access to the RAG playground frontend. No major bugs reported this month; the focus was on documentation and enablement. Business value: accelerates customer onboarding and reduces deployment risk by providing a reproducible, production-ready AKS-based RAG workflow. Technologies demonstrated: AKS, NVIDIA NIMs, NeMo Retriever, Milvus, LLMs, embeddings, reranking, infrastructure as code, deployment automation, and frontend integration.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 (2025-04): Delivered the RAG AKS Deployment Workshop Guide for NVIDIA/nim-deploy. This end-to-end guide documents deploying a Retrieval Augmented Generation stack on Azure Kubernetes Service using NVIDIA NIMs and NeMo Retriever, including LLM, embedding, and reranking microservices with Milvus as the vector store. It covers infrastructure deployment steps, NVIDIA software integration, and access to the RAG playground frontend. No major bugs reported this month; the focus was on documentation and enablement. Business value: accelerates customer onboarding and reduces deployment risk by providing a reproducible, production-ready AKS-based RAG workflow. Technologies demonstrated: AKS, NVIDIA NIMs, NeMo Retriever, Milvus, LLMs, embeddings, reranking, infrastructure as code, deployment automation, and frontend integration.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for NVIDIA/nim-deploy: Delivered a production-grade, end-to-end air-gapped deployment workflow for Llama 3.1 70B on Azure Machine Learning using TensorRT. The workflow includes Azure resource provisioning, secure NGC API key handling, offline caching of the NIM model, and deployment as a managed online endpoint with a testable endpoint (Gradio optional). Also fixed a deployment reliability issue by correcting the Azure VM instance type string in the deployment notebook to prevent failures. This work demonstrates the ability to deliver secure, scalable, and testable ML deployments in cloud environments, with traceable commits and clear operational handoffs.
2 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for NVIDIA/nim-deploy: Delivered a production-grade, end-to-end air-gapped deployment workflow for Llama 3.1 70B on Azure Machine Learning using TensorRT. The workflow includes Azure resource provisioning, secure NGC API key handling, offline caching of the NIM model, and deployment as a managed online endpoint with a testable endpoint (Gradio optional). Also fixed a deployment reliability issue by correcting the Azure VM instance type string in the deployment notebook to prevent failures. This work demonstrates the ability to deliver secure, scalable, and testable ML deployments in cloud environments, with traceable commits and clear operational handoffs.
January 2025
Activity
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Quality Metrics
Correctness93.0%
Maintainability89.2%
Architecture90.0%
Performance83.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashJSONJupyter NotebookMarkdownPythonShellYAMLbashmarkdown
Technical Skills
API IntegrationAzureAzure Machine LearningCLICloud ComputingContainerizationDevOpsDockerDocumentationHelmInfrastructure as CodeInfrastructure as Code (IaC)KubernetesLLM DeploymentMachine Learning Deployment
Repositories Contributed To
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