Analyzes post-deployment ML issues and categorizes them to advocate for better monitoring tools, using Zillow's case as an example.
Explores semi-supervised learning techniques for training models when labeled data is scarce, focusing on combining labeled and unlabeled data.
The author introduces ApplyingML.com, a site dedicated to sharing practical knowledge and interviews on applying machine learning effectively in real-world work.
Learn how to integrate the Hugging Face Hub as a model registry with Amazon SageMaker for MLOps, including training and deployment.
A guide to attending AWS re:Invent 2021 machine learning and NLP sessions remotely, featuring keynotes and top session recommendations.
A tutorial on deploying the BigScience T0_3B language model to AWS and Amazon SageMaker for production use.
Advises starting ML projects with simple heuristics and data analysis before implementing complex machine learning models, citing expert advice.
A talk on system design principles for building production recommendation systems and search engines, presented at an MLOps Community meetup.
A forecast of speech recognition technology's evolution from 2010 to 2030, analyzing past progress and predicting future trends.
Profile of Amazon applied scientist Eugene Yan, focusing on his career in data science and his influential technical writing about machine learning.
Explores methods like semi-supervised and active learning to create training labels when labeled datasets are unavailable, with industry examples.
Explains how to bootstrap training labels for a semantic search system using initial lexical search and user click data instead of costly human annotation.
Highlights ICML 2021 invited talks on applying machine learning to scientific domains like drug discovery, climate science, poverty alleviation, and neuroscience.
A talk on system design for recommendation and search systems, covering architecture and production considerations.
An in-depth technical explanation of diffusion models, a class of generative AI models that create data by reversing a noise-adding process.
A comprehensive deep learning course covering fundamentals, neural networks, computer vision, and generative models using PyTorch.
A comprehensive deep learning course overview with PyTorch tutorials, covering fundamentals, neural networks, and advanced topics like CNNs and GANs.
A data scientist shares practical strategies and mindsets for influencing technical teams and driving change without formal authority.
Analyzes the legal implications of GitHub Copilot potentially being a derivative work of GPL-licensed code used in its training.
Explores system design patterns for industrial-scale recommendation and search engines, focusing on offline/online components and retrieval/ranking stages.
