# MLflow Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/mlflow
> Markdown URL: https://aitinkerers.org/technologies/mlflow.md
> Technology record last updated: 2026-02-25T14:43:34Z
> Generated: 2026-09-22T04:43:51Z

MLflow is the open-source platform for managing the complete machine learning lifecycle: tracking, reproducibility, and deployment.

MLflow standardizes the ML workflow using four core components. **MLflow Tracking** logs experiment details, recording parameters, metrics (like accuracy), and artifacts for every run. **MLflow Projects** package code in a reusable format, ensuring reproducibility across environments via a simple `mlflow run` command. **MLflow Models** provide a consistent model format ('flavor') for deployment, supporting frameworks like scikit-learn, PyTorch, and TensorFlow. Finally, the **MLflow Model Registry** centralizes model management, handling versioning and stage transitions (e.g., Staging to Production) for governance and collaboration.

- Official technology site: https://mlflow.org/
- Public AI Tinkerers demos and talks: 5
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Pista Inteligente: Cómo construí un sistema de ranking de carreras de caballos con LightGBM, Claude como extractor de datos y calibración isotónica](https://santiago.aitinkerers.org/talks/rsvp_EiElZqyICcA)

Construí Pista Inteligente, un pipeline de Machine Learning end-to-end que predice el orden de llegada de caballos en carreras del Club Hípico de Santiago e Hipódromo Chile. El sistema convierte programas de carreras en papel (PDFs y texto) en CSV estructurado usando prompts especializados con Claude. Esos datos alimentan un modelo LightGBM Ranker (LambdaRank) con Walk-Forward Validation que ordena los caballos por probabilidad de victoria. Las probabilidades crudas del modelo pasan por calibración isotónica para que los scores sean realmente interpretables como probabilidades. La arquitectura completa está en Python con mlflow para tracking de experimentos.

- Event context: 🔥 AI TINKERERS - SANTIAGO / CAPÍTULO #10 / 2026 — 2026-04-16 — Santiago
- Public talk page: https://santiago.aitinkerers.org/talks/rsvp_EiElZqyICcA

### [Escape the Sequential Training Trap: 16x Higher Throughput for LLM Experimentation](https://san-diego.aitinkerers.org/talks/rsvp_elLipYwoqic)

I'll be presenting RapidFire AI, a new open-source framework that transforms LLM fine-tuning and post-training from sequential one-config-at-a-time training into hyperparallelized experimentation with dynamic real-time experiment control and automatic multi-GPU orchestration. The core innovation is an adaptive execution engine that allows for multiple configs to be compared on even a single GPU by automatically chunking the data into subsets and cycling configs across them via a new shared memory subsystem. It enables “Interactive Control Operations” - dynamic modification of running experiments. Stop underperforming configurations, clone promising ones, and warm-start variants from parent checkpoints. The RapidFire AI scheduler intelligently manages multi-GPU orchestration to optimize GPU utilization and uses FSDP automatically for sharding large models across GPUs. The framework supports multiple popular LLM customization workflows from Hugging Face TRL, including SFT, DPO, and GRPO.

- Event context: San Diego October Meetup w/ Structured — 2025-10-30 — San Diego
- Public talk page: https://san-diego.aitinkerers.org/talks/rsvp_elLipYwoqic

### [A wolf in sheep's clothing](https://medellin.aitinkerers.org/talks/rsvp_ibWb5rqYBE8)

Camuflarse es una de las habilidades más poderosas en múltiples ámbitos: implica ocultar o disimular rastros con distintos fines, ya sea para despistar competidores, proteger secretos o simplemente mantenerse oculto. En el terreno de Machine Learning, esta habilidad adquiere nuevos matices gracias a los modelos generativos, que logran construir camuflajes con un realismo antes inimaginable. Ahora, imagina trasladar esta idea al mundo de la ciberseguridad, tener la capacidad de ocultar un ataque como un evento benigno para pasar desapercibido, tener la capacidad de ser un Lobo vestido de Oveja.

- Event context: AI Tinkerers Medellín #14 - Wizeline - 26 de Junio, 2025 — 2025-06-26 — Medellín
- Public talk page: https://medellin.aitinkerers.org/talks/rsvp_ibWb5rqYBE8

### [Evaluating a Medical Assistant](https://poland.aitinkerers.org/talks/rsvp_NPP2O6J7Te4)

How do you evaluate an AI system that assists doctors with medical documentation? In this talk, we'll share practical insights from building an evaluation framework for Noa Notes @ Docplanner - a system that transcribes and summarizes doctor-patient conversations. We will discuss our two-tier evaluation approach combining detailed factual assessment with style analysis, see how we leverage LLMs in the evaluation pipeline, and share specific examples of how prompt engineering improved our metrics. We'll also discuss challenges unique to the medical domain and how we addressed them.

- Event context: AI Tinkerers Poland - Second Meetup in Warsaw (January) — 2025-01-30 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_NPP2O6J7Te4

### [AutoML Agent: Automating ML Code Generation](https://toronto.aitinkerers.org/talks/rsvp_D5m-tfkILPA)

This demo will showcase a custom proof of concept AI Agent that autonomously generates and executes Python code to find the best machine learning model for a given problem. Built from scratch this agent addresses limitations common in agentic frameworks like Langchain or CrewAI, providing enhanced flexibility and control for production-grade applications. 1. Live Demo We'll begin with a live Colab notebook demo, which attendees can access to try the application during the presentation or later. Built entirely in Python with open-source libraries and LLMs, this demo enables hands-on participation. 2. Key Components We’ll then walk through the application’s architecture, covering: State Management: Using the transitions library, we manage the state machine workflow to handle tasks such as entity extraction, code generation, error handling, and re-execution, ensuring modular and resilient task transitions. Open-Source LLM: We use an open source Phi 3.5 model for core tasks like code generation, entity extraction, and error management, demonstrating the capabilities of open-source LLMs for AutoML. 3. Production Architecture Overview Finally, a production-ready architecture, visualized on Miro, demonstrates an end-to-end production solution on AWS, including: Frontend and API Layer: Allowing user interaction through a secure, scalable API and frontend interface. Load Balancing and Model Deployment: AWS Lambda with load balancing optimize for scalable, responsive model deployment. Security: VPCs, and user verification via AWS Cognito ensure controlled access and data security. Logging and Monitoring: Model monitoring and tracing via MLFLow on AWS Sagemaker.

- Event context: AI Tinkerers Toronto - November 2024 Meetup at Accenture — 2024-11-28 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_D5m-tfkILPA

## Related Technologies

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- [BLOOM](https://aitinkerers.org/technologies/bloom) ([Markdown](https://aitinkerers.org/technologies/bloom.md)) — 115 public demos
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