machine-learning-ops

Declarative Intent Driven Platform Orchestrator for Internal Developer Platform (IDP).

Notes for Machine Learning Engineering for Production (MLOps) Specialization course by DeepLearning.AI & Andrew Ng

Track experiments, log metrics, manage models — MLflow from Julia.

Connecting MLJ and MLFlow

Hands-on AI infrastructure projects for DevOps, SRE, and platform engineers. Learn MLOps, LLMOps, and GPU-on-Kubernetes by building real systems — model serving with vLLM, ML pipelines, GPU autoscaling, and production inference platforms.

Serve machine learning models in Elixir. Production ML inference for Phoenix and the BEAM: OTP supervision, worker pools, dynamic batching, caching, telemetry and zero-downtime canary rollout around any backend — Nx, Bumblebee, ONNX, Python or a remote service.

Fine-tuning Mistral-7B for domain-specific support using QLoRA; featuring automated evaluation (ROUGE/BLEU) and a production-ready FastAPI inference engine.

Kueski Challenge - Vacante de Machine Learning Engineer

Sliced metrics, failure triage, and CI regression gating for object detection models. Bring your COCO labels and predictions — get back a breakdown by object size, clutter, and lighting. pip install perceptorguard

Kubernetes operator (Kubebuilder/controller-runtime) for LLMInferenceCluster: prefill/decode workloads, KV shard map + session-aware router, mock autoscaling signals, and Prometheus/Grafana for kind demos.

A machine learning project to detect online gambling comments using an automated ML pipeline.

LLM Fine-Tuning Experiment Tracker - FastAPI backend with job orchestration, real-time metrics, and pluggable compute providers

Lavagante Quantitative Research Framework - BETA | Experimental exploration of quantum-inspired algorithms for financial modeling. Seeking academic collaboration and community feedback for research validation. Educational resource in development.