Deep Learning has a strong open-source culture. Many great learning resources exist on blogs, lectures, tutorials, newsletters, course websites, and code repositories.
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- 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)
- Continuous delivery and automation pipelines in machine learning (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)