Further reading
To further your knowledge, please consult the following resources and documentation:
- On the Opportunities and Risks of Foundation Models (Stanford University): https://arxiv.org/abs/2108.07258
- MLOps: not as Boring as it Sounds: https://itnext.io/mlops-not-as-boring-as-it-sounds-eaebe73e3533
- AI is Driving Software 2.0… with Minimal Human Intervention: https://www.datasciencecentral.com/profiles/blogs/ai-is-driving-software-2-0-with-minimal-human-intervention
- MLOps: Continuous delivery and automation pipelines in machine learning (Google): https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
- Deep Learning Development Cycle (Salesforce): https://metamind.readme.io/docs/deep-learning-dev-cycle
- MLOps – The Missing Piece In The Enterprise AI Puzzle: https://www.forbes.com/sites/janakirammsv/2021/01/05/mlopsthe-missing-piece-in-the-enterprise-ai-puzzle/?sh=3d5c89dd24ad
- MLOps: What It Is, Why It Matters, and How to Implement It: https://neptune.ai/blog/mlops
- Explainable Deep Learning: A Field Guide for the Uninitiated: https://arxiv.org/abs/2004.14545
- Machine learning explainability is just the beginning: https://truera.com/machine-learning-explainability-is-just-the-beginning/
- AI Fairness — Explanation of Disparate Impact Remover: https://towardsdatascience.com/ai-fairness-explanation-of-disparate-impact-remover-ce0da59451f1
- Datasheets for Datasets: https://arxiv.org/pdf/1803.09010.pdf
