M

mlops

Bootstrap a self-service AI platform with GitOps

Hybrid cloud-edge ML system for predictive rain control with automated retraining, monitoring, and Raspberry Pi hardware actuation.

This project is home to MLOps for Platform. Continuous Learning (CL). Continuous Training (CT)

CICD project for a sentiment analyzer (deployment on lightining.ai gpu)

A generic implementation roadmap that can facilitate MLOps for any ML problem in detail. This roadmap is intended for reducing problem solutioning, and using problem solving instead.

Enterprise-grade Medical AI Platform with FastAPI, Kubernetes, Monitoring and AKS-ready deployment architecture.

Production-ready RAG starter: hybrid search, chunking strategies, observability (Prometheus/Grafana), MLflow tracking, drift detection, GDPR deletion, and evaluation. The parts the tutorials skip.

Automated LLM Benchmarking on GPU - tokens/sec, latency percentiles, VRAM profiling, multi-format support (HuggingFace, GGUF, GPTQ)

https://ayinedjimi-consultants.fr

Automated Dataset Creation & Publishing Pipeline - Scrape, clean, transform, validate and publish datasets to HuggingFace Hub

https://ayinedjimi-consultants.fr

Hybrid credit intelligence platform using XGBoost, RAG, Terraform and GitLab CI/CD.

Check game monitor and download game here: https://omni-synesis.onrender.com/

📝 The "EduAvenues" Project Description "A production-grade MLOps prototype for automated student essay evaluation. This system leverages 30 years of pedagogical expertise to audit LLM outputs using Chain-of-Thought (CoT) and RAG, ensuring academic rigor and trust in AI-driven grading pipelines."

Public pages of the ANR project "FATES-MLOps"

Full MLOps: DVC + MLflow + GitLab CI + K8s + Monitoring

This is the code repository associated with my development notes notebook.

I need it to improve development efficiency. I study concepts, prototype solutions, save them here along with their comments. When needed, I know where I can quickly find code examples to reuse.

Simulation of a real hospital scenario with a ML model in production