MLOps- 吴恩达Andrew Ng Data Definition and Baseline Week3 论文等资料汇总

Posted 2021-09-20 架构师易筋

论文和引用

Introduction to Machine Learning in Production

This is a compilation of resources including URLs and papers appearing in lecture videos.

Overall resources:

• Konstantinos, Katsiapis, Karmarkar, A., Altay, A., Zaks, A., Polyzotis, N., … Li, Z. (2020). Towards ML Engineering: A brief history of TensorFlow Extended (TFX). http://arxiv.org/abs/2010.02013

• Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2020). Challenges in deploying machine learning: A survey of case studies. http://arxiv.org/abs/2011.09926

Week 1: Overview of the ML Lifecycle and Deployment

• Concept and Data Drift

• Monitoring ML Models

• A Chat with Andrew on MLOps: From Model-centric to Data-centric AI

• Konstantinos, Katsiapis, Karmarkar, A., Altay, A., Zaks, A., Polyzotis, N., … Li, Z. (2020). Towards ML Engineering: A brief history of TensorFlow Extended (TFX). http://arxiv.org/abs/2010.02013

• Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2020). Challenges in deploying machine learning: A survey of case studies. http://arxiv.org/abs/2011.09926

• Sculley, D., Holt, G., Golovin, D., Davydov, E., & Phillips, T. (n.d.). Hidden technical debt in machine learning systems. Retrieved April 28, 2021, from Nips.cc

https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf

Week 2: Select and Train Model

• Establishing a baseline

• Error analysis

• Experiment tracking

• Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., … Anderljung, M. (n.d.). Toward trustworthy AI development: Mechanisms for supporting verifiable claims∗. Retrieved May 7, 2021 http://arxiv.org/abs/2004.07213v2

• Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., & Sutskever, I. (2019). Deep double descent: Where bigger models and more data hurt. Retrieved from http://arxiv.org/abs/1912.02292

Week 3: Data Definition and Baseline

• Label ambiguity

• https://arxiv.org/pdf/1706.06969.pdf

• Data pipelines

• Data lineage

• MLops

Geirhos, R., Janssen, D. H. J., Schutt, H. H., Rauber, J., Bethge, M., & Wichmann, F. A. (n.d.). Comparing deep neural networks against humans: object recognition when the signal gets weaker∗. Retrieved May 7, 2021, from Arxiv.org website:

参考

https://www.deeplearning.ai/program/machine-learning-engineering-for-production-mlops/