Where to go next
Deep Learning has a strong open-source culture. Many great learning resources exist on blogs, lectures, tutorials, newsletters, course websites, and code repositories.
This section is mostly up to you! Submit a pull request to add helpful resources.
Conferences
ScaledML (by Matroid)
MLOps Conference (by Iguazio)
Spark + AI Summit (by Databricks)
Newsletters
The Batch (by deeplearning.ai)
Machine Learning In Production (by Luigi Patruno)
Import AI (by Jack Clark)
The Machine Learning Engineer Newsletter (by The Institute for Ethical AI & ML)
Projects To Know (by Amplify Partners)
Personal Blogs
Locally Optimistic (Data Leaders in NYC)
MLOps Tooling Landscape (by Chip Huyen)
Three Risks in Building Machine Learning Systems (by Benjamin Cohen)
How To Serve Models (by Bugra Akyildiz)
Nitpicking ML Technical Debt (by Matthew McAteer)
Monitoring ML Models in Production (by Christopher Samiullah)
Models for integrating data science teams within organizations (Pardis Noorzad)
Data-as-a-Product vs Data-as-a-Service (Justin Gage)
Corporate Blogs
The New Business of AI (by a16z)
Long-Tailed AI Problems (by a16z)
Rules of ML (by Google)
Tecton: The Data Platform for Machine Learning (by Tecton)
Why We Need DevOps for ML Data (by Tecton)
Continuous Delivery for Machine Learning (by ThoughtWorks)
Dagster: The Data Orchestrator (by Elementl)
State of Machine Learning Model Servers In Production (by Anyscale)
Repositories
Awesome Production Machine Learning (by The Institute for Ethical AI & Machine Learning)
MLOps References (by InnoQ)
ML Applied in Production (by Eugene Yan)
Feature Stores for ML (by KTH Royal Institute of Technology)
Feature Store: The Missing Data Layer in ML Pipelines? (by Logical Clocks)
Tutorials
Image Retrieval in the Wild (CVPR 2020)
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