A modular, beginner-to-expert guide to fine-tuning both OpenAI GPT models and open-source LLMs/SLMs on Azure - designed for technical and non-technical users alike.

This hands-on walks you through fine-tuning an open source LLM on Azure and serving the fine-tuned model on Azure. It is intended for Data Scientists and ML engineers who have experience with fine-tuning but are unfamiliar with Azure ML.

A modular, beginner-to-expert guide to fine-tuning both OpenAI GPT models and open-source LLMs/SLMs on Azure - designed for technical and non-technical users alike.

This lab is a starter for quickly and easily applying SLM/LLM fine-tuning, evaluation, and quantization with torchtune on Azure ML.

This hands-on walks you through fine-tuning an open source LLM on Azure and serving the fine-tuned model on Azure. It is intended for Data Scientists and ML engineers who have experience with fine-tuning but are unfamiliar with Azure ML.

A low latency parser and chatbot engine leveraging Azure OpenAI Embeddings with 2 custom fine-tuned LLMs, achieving ultra-low 100ms delay via MongoDB Atlas Vector Search and Redis while storing the uploaded documents on AWS S3. Deployed the FastAPI and Streamlit images with Jenkins pipelines on Azure Container Apps, reducing 60% peak loads.

Built an end-to-end Generative AI app by fine-tuning an LLM with Azure AI Foundry and integrating it into a React web interface. Set up automated CI/CD using AWS CodePipeline and deployed on S3 for scalable delivery. Focused on production-ready AI workflows combining GenAI, cloud infrastructure, and DevOps practices

Technical demo for fine-tuning the LLM using Microsoft Foundry's no-code Azure AI portal to create a specialized SAP S/4HANA Finance Agent that improves accuracy and domain relevance for SAP FI/CO queries through Low-Rank Adaptation (LoRA)

Advanced n8n AI agent with custom fine-tuned LLM powered by Azure student credits

Real-time AIOps log analysis with fine-tuned LLM (PEFT+LoRA). Supports AWS/Azure ingestion, Kafka streaming, VictoriaMetrics storage, anomaly detection & smart Gmail/Teams alerts. Fully containerized with Docker Compose, Zookeeper & Grafana dashboards.

Hands-on Azure ML & Microsoft AI Foundry notebooks and artifacts: training, MLflow, pipelines, endpoints, RAI dashboards, privacy/smart noise, RAG with Azure AI Search, LLM fine-tuning, plus cheatsheets for ML/LLM metrics and core concepts.

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Implemented fine-tuning of a language model using Azure OpenAI for specialized conversational capabilities. Utilized custom training datasets to enhance model performance in generating contextual responses. Managed and monitored fine-tuning processes to optimize model accuracy and efficiency, leveraging Azure OpenAI's advanced capabilities.

Applied Azure-based LLM fine-tuning workflows to generate structured IC netlists from input parameters.

wazuh azure security-operations siem mistral llm hipaa terraform aks mlops soc open-source azure-api-management lora fine-tuning

an LLm project using Angular and .Net core to fine tune Azure OpenAI gpt models

Generate PySpark queries from natural language using RAG or fine-tuned LLMs, deployable within a private Azure network

A hands-on LLM fine-tuning and RAG experimentation repository covering Gemma and LLaMA-2 fine-tuning (LoRA & full FT), dataset creation, Hugging Face workflows, Azure integration, sentiment analysis, and end-to-end RAG prototypes built in Colab and Python.

A public library demonstrating my AI management skills, including frameworks (PyTorch, TensorFlow), AI concepts (LLM fine-tuning, prompt engineering), and infrastructure (AWS, Azure, vector databases).

Data Scientist & GenAI Engineer | RAG pipelines, LLM fine-tuning (LoRA/QLoRA), MLOps with MLflow & Azure ML, NLP with BERT & Hugging Face, PySpark data engineering. 5+ years building production-grade AI/ML systems on Azure.

Enterprise AI platform for the energy sector, featuring LoRA fine-tuning, domain-specific LLM workflows, and Azure ML deployment. Includes data generation, RAG, and sector-adapted model orchestration for production, safety, and optimization use cases.

Automated MLOps pipeline built in n8n. Orchestrates the end-to-end lifecycle of LLM fine-tuning: collects training data, validates JSONL formats, uploads to provider APIs (OpenAI/Azure), and triggers training jobs without manual CLI intervention.

LLM Fine-tuning & RAG · Optimisation · Prompt Engineering · Sécurité · NLP · Embeddings · Vision · Reinforcement Learning MLOps CI/CD · Docker · Kubernetes · MLflow · Weights & Biases · Monitoring Cloud AWS · GCP · Azure · Serveurs on-premise Dev Python · FastAPI · Node.js · LangChain · Hugging Face · Git · REST · SQL/NoSQL