Engineering posts about Supervised Learning
Curated summaries and key learnings for engineers working with Supervised Learning.
A Practical Guide to LLM Fine Tuning
This article serves as a practical guide for ML engineers and AI practitioners focused on fine-tuning large language models (LLMs) for specific tasks. It outlines the entire lifecycle of LLM...
MaxText Expands Post-Training Capabilities: Introducing SFT and RL on Single-Host TPUs
The article introduces new post-training capabilities in MaxText, specifically Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) optimized for single-host TPU configurations. It highlights...
Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments
The article presents a study on enhancing search relevance in app store rankings by integrating LLM-generated judgments. It identifies the challenge of limited expert-provided textual relevance...
Using LLMs to amplify human labeling and improve Dash search relevance
The article outlines how Dropbox Dash utilizes a retrieval-augmented generation (RAG) approach to enhance search relevance by integrating large language models (LLMs) with human labeling. It explains...
Reusing Pre-Training Data at Test Time is a Compute Multiplier
The article explores the potential of reusing pre-training data during test time to enhance the performance of large language models (LLMs). It highlights the inefficiencies in current pre-training...
LLM-Powered Relevance Assessment for Pinterest Search
The article presents a methodology employed by Pinterest Search to enhance search relevance assessment using fine-tuned large language models (LLMs). It addresses the challenges of traditional human...
Improving Quality of Recommended Content through Pinner Surveys
The article discusses Pinterest's innovative approach to enhancing the quality of recommended content through user feedback collected via surveys. By leveraging machine learning models trained on...
New serverless customization in Amazon SageMaker AI accelerates model fine-tuning
The article introduces new serverless customization features in Amazon SageMaker AI, allowing users to fine-tune popular AI models efficiently. It highlights an easy-to-use interface for selecting...