Subject
8 entries
Production Ml
Bookmarks
Made With ML: MLOps Curriculum
Made With ML is a free, project-based curriculum for learning ML engineering and MLOps — covering not just model training but the full production pipeline from data to deployment. One of the most practical and comprehensive self-study resources for applied ML.
Exploring Production-Ready Recommender Systems with NVIDIA Merlin
NVIDIA Merlin is a framework for building GPU-accelerated production recommender systems — covering feature engineering (NVTabular), training (HugeCTR, Merlin Models), and serving (Triton). This post explores the end-to-end pipeline for large-scale recommendation.
MLOps Maturity Models: Google and Microsoft Frameworks
ZenML's overview of MLOps maturity models from Google and Microsoft — frameworks for thinking about how ML organizations can systematically improve how they develop and deploy models. Useful if you're trying to level up a team's ML practices from ad-hoc to automated.
Full Stack Deep Learning (Spring 2021)
Full Stack Deep Learning is a free course bridging ML research and production deployment — covering the full pipeline from data management and model training to testing, monitoring, and team structures. The course for researchers who want to ship and engineers who want to understand ML.
applied-ml: Papers and Tech Blogs on ML in Production
Eugene Yan's curated list of papers and engineering blog posts from companies sharing real-world ML in production — classification, recommendation, search, NLP, and more. One of the most useful ML reference repositories because it focuses on what actually shipped, not just what was published.
ML in Production — Best Practices for Real-World ML Systems
ML in Production is a blog and newsletter focused on building and operating real-world ML systems — covering experimentation programs, deployment, monitoring, and the organizational practices that make ML succeed in production environments.
Applied ML: Papers and Blogs on ML in Production (2020)
Eugene Yan's curated GitHub list of papers and blog posts on ML in production — original 2020 bookmark of this now-landmark repository. Covers recommendation, search, NLP, data quality, feature engineering, and more from companies that actually shipped these systems.
Reflecting on a Year of Making Machine Learning Actually Useful
Shreya Shankar's honest reflection on a year trying to make machine learning actually useful in industry — covering the gap between academic ML and production, the underappreciated role of data work, and why most ML projects fail before the model stage. One of the most cited personal essays in the MLOps space.