Further reading
For more information on the topics that were covered in this chapter, please take a look at the following suggested books and links:
- Automated ML: Methods, Systems, Challenges, by Frank Hutter (Editor), Lars Kotthoff (Editor), and Joaquin Vanschoren (Editor)
- The Springer Series on Challenges in ML
- Hands-On Automated ML: A beginner's guide to building automated ML systems using AutoML and Python, bby Sibanjan Das and Umit Mert Cakmak, Packt
- Auto XGBoost: https://github.com/ja-thomas/autoxgboost
- RapidMiner: https://rapidminer.com/products/auto-model/
- BigML: https://bigml.com/
- MLJar: https://mljar.com/
- MLBOX: https://github.com/AxeldeRomblay/MLBox
- DataIKU: https://www.dataiku.com/
- Awesome-AutoML-Papers by Mark Lin: https://github.com/hibayesian/awesome-automl-papers
- Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA: https://www.cs.ubc.ca/labs/beta/Projects/autoweka/
- Auto-Keras: An Efficient Neural Architecture Search System: https://arxiv.org/pdf/1806.10282.pdf
- A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead. Doris Jung-Lin Lee et al.: dorisjunglinlee.com/files/MILE.pdf
- What is Data Wrangling and Why Does it Take So Long? by Mike Thurber: https://www.elderresearch.com/blog/what-is-data-wrangling
- Efficient and Robust Automated ML: http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf
- LinkedIn Workforce Report: https://economicgraph.linkedin.com/resources/linkedin-workforce-report-august-2018
