- A few articles with great of tips about how to design ML-powered products:
- https://medium.com/s/story/notes-to-myself-on-software-engineering-c890f16f4e4d
- https://medium.com/@davidbessis/building-ai-first-products-90d503ccd43a
- https://medium.com/@deepmindsafetyresearch/building-safe-artificial-intelligence-52f5f75058f1
- https://medium.com/@rchang/getting-better-at-machine-learning-16b4dd913a1f
- https://arxiv.org/pdf/1810.03993.pdf
- https://blog.insightdatascience.com/how-to-deliver-on-machine-learning-projects-c8d82ce642b0
- https://blog.insightdatascience.com/an-introduction-to-the-data-product-management-landscape-ef930afe6de5
- https://medium.com/the-lever/retracing-your-steps-in-machine-learning-ml-versioning-74d19a66bd08
- https://medium.com/the-lever/no-machine-learning-in-your-product-start-here-2df776d10a5c
- https://medium.com/the-lever/data-a-key-requirement-for-your-machine-learning-ml-product-9195ace977d4
- https://anatomyof.ai/
- https://www.oreilly.com/ideas/lessons-learned-turning-machine-learning-models-into-real-products-and-services
- http://www.bradfordcross.com/blog/2017/6/13/vertical-ai-startups-solving-industry-specific-problems-by-combining-ai-and-subject-matter-expertise
- https://www.wired.com/story/how-amazon-taught-alexa-to-speak-french/
- http://treycausey.com/software_dev_skills.html
- A few articles/tutos with good tips on how to ship ML-powered products:
- http://www.marknagelberg.com/digging-into-data-science-tools-docker/
- https://medium.com/analytics-vidhya/deploy-your-first-deep-learning-model-on-kubernetes-with-python-keras-flask-and-docker-575dc07d9e76
- https://towardsdatascience.com/deploying-a-machine-learning-model-as-a-rest-api-4a03b865c166
- https://blog.keras.io/user-experience-design-for-apis.html
- https://medium.com/@maheshkkumar/a-guide-to-deploying-machine-deep-learning-model-s-in-production-e497fd4b734a
- https://medium.com/google-cloud/keras-inception-v3-on-google-compute-engine-a54918b0058
- https://arxiv.org/pdf/1810.09591.pdf
- https://www.liip.ch/en/blog/numbers-recognition-mnist-on-an-iphone-with-coreml-from-a-to-z
- https://www.raywenderlich.com/577-core-ml-and-vision-machine-learning-in-ios-11-tutorial
- https://www.datacamp.com/community/tutorials/machine-learning-models-api-python
- http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
- https://blog.dominodatalab.com/data-science-vs-engineering-tension-points/
- https://github.com/hadley/stats337
- https://towardsdatascience.com/i-worked-with-a-data-scientist-heres-what-i-learned-2e19c5f5204
- https://blog.insightdatascience.com/how-to-deliver-on-machine-learning-projects-c8d82ce642b0
- https://towardsdatascience.com/data-science-project-flow-for-startups-282a93d4508d
- https://medium.com/techking/human-like-playtesting-with-deep-learning-92adafffe921
- https://emilygorcenski.com/post/data-versioning/
- Docker & Serverless:
- https://towardsdatascience.com/learn-enough-docker-to-be-useful-b7ba70caeb4b
- https://towardsdatascience.com/learn-enough-docker-to-be-useful-1c40ea269fa8
- https://mike.place/talks/serverless/
- A few ML-powered projects I'd like to implement when I find time:
- http://norvig.com/spell-correct.html
- https://vdutor.github.io/blog/2018/05/07/TF-rex.html
- https://medium.com/@dhruvp/how-to-write-a-neural-network-to-play-pong-from-scratch-956b57d4f6e0
- https://www.liip.ch/en/blog/numbers-recognition-mnist-on-an-iphone-with-coreml-from-a-to-z
- https://eng.uber.com/michelangelo-pyml/
- https://medium.com/@adriensieg/create-a-full-search-engine-via-flask-elasticsearch-javascript-d3js-and-bootstrap-275f9dc6efe1
- https://medium.com/@dhruvp/how-to-write-a-neural-network-to-play-pong-from-scratch-956b57d4f6e0
- https://zyjerah.github.io/Frame-of-Fancy/
- https://medium.com/jettech/https-medium-com-jettech-visual-search-hayneedle-f248aa05f2f2
- A bit more general about product management:
- https://medium.com/@gabriel_31154/product-management-on-the-dreem-headband-e8e2e107144
- https://hackernoon.com/why-you-should-be-data-informed-and-not-data-driven-76079d187989
- http://firstround.com/review/17-product-managers-who-will-own-the-future-of-nyc-tech-and-the-9-frameworks-theyll-use-to-do-it/
- https://medium.com/@diemkay/how-i-prepared-for-a-product-manager-interview-26122f2c80ba
- https://medium.com/airbnb-engineering/experimentation-measurement-for-search-engine-optimization-b64136629760
- Career advice in Data (& Computer) Science / Job interviews tips:
- https://huyenchip.com/2018/10/08/career-advice-recent-cs-graduates.html
- http://www.cs.cmu.edu/~harchol/gradschooltalk.pdf
- http://hookedondata.org/Red-Flags-in-Data-Science-Interviews/
- http://hookedondata.org/Advice-for-Applying-to-Data-Science-Jobs/
- https://www.analyticsvidhya.com/blog/2018/06/comprehensive-data-science-machine-learning-interview-guide/
- https://medium.com/@rchang/my-two-year-journey-as-a-data-scientist-at-twitter-f0c13298aee6
- https://caitlinhudon.com/2018/01/19/imposter-syndrome-in-data-science/
- http://chaire-arts-sciences.org/
- https://towardsdatascience.com/why-you-shouldnt-be-a-data-science-generalist-f69ea37cdd2c
- https://angel.co/blog/30-questions-to-ask-before-joining-a-startup
- Privacy & Security in Machine-Learning:
- https://arxiv.org/pdf/1811.01134.pdf
- https://arxiv.org/pdf/1602.05629.pdf
- https://ai.googleblog.com/2017/04/federated-learning-collaborative.html?m=1
- https://robertovitillo.com/2016/07/29/differential-privacy-for-dummies/
- http://www.cleverhans.io/privacy/2018/04/29/privacy-and-machine-learning.html
- Misc.: