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.
​ScaledML (by Matroid)
​MLOps Conference (by Iguazio)
​Spark + AI Summit (by Databricks)
​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)
​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)
​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)
​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)
​Image Retrieval in the Wild (CVPR 2020)
​Handwritten Text Recognition (OCR) with MXNet Gluon (AWS Labs)
