Rocket Money x Hugging Face: Scaling Volatile ML Models in Production​

Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets

Linear models with an $L_1$-norm penalty remain state-of-the-art for high-dimensional ($d > 1,000,000$) tasks, offering a straightforward method for solving real-world industry problems. Despite their...

Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets

Linear models with an $L_1$-norm penalty are still the leading approach for high‑dimensional tasks, yet many existing solvers are slow, ineffective, and hard to parallelise, making them unsuitable for large industry‑scale corpora. The paper evaluates several recent state‑of‑the‑art methods and shows that older techniques outperform them in general use. It also demonstrates that a simple baseline—LBFGS applied to a sub‑gradient with minor tweaks—yields strong performance and is easier to support and scale in production.

Investing in Performance: Fine-tune small models with LLM insights - a CFM case study

Model Distillation in the API

Fine-tune a cost-efficient model with the outputs of a large frontier model–all on the OpenAI platform

GaLore: Advancing Large Model Training on Consumer-grade Hardware

Design Once, Deploy at Scale: Template-Driven ML Development for Large Model Ecosystems

arXiv:2603. 24963v3 Announce Type: replace Abstract: Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversion rates, and other optimization events.