Workera，作为一个deeplearning.ai旗下的公司，提供了一份关于AI行业中不同岗位的技能和工具的报告，英文原版在此下载。对于想要进入AI领域的局外人了解这些岗位区别，规划职业发展有很大帮助。

做机器学习的时候要想清楚自己的目标是什么。是获得模型，还是获得预测能力。
前者是统计学家，后者是数据科学家。

If your model doesn’t have the same performances on the training set and in the live environment is not a matter of trust, but a problem either in your dataset or in your testing framework. Trust is built on performances and performances on metrics: design the ones that work for your problem and stick to them. If you’re looking for trust in interpretability you’re just asking to the model questions you already know the answers, and you want them to be provided in the exact way you are expecting them. Do you need machine learning for building such a system? The need of ML arises when you know questions and answers but you don’t know an easy way to get from one to the others. We need a technique to fake the process, and it might be that an easy explanation for it doesn’t even exist.

（略）

I’m not a fanboy, and the more I know about machine learning, trying to build some real products out of it, the most I loose interest in this kind of discussions. Probably, the only useful thing about ML is in its ability to replicate processes that aren’t easy to describe explicitly: you just need questions and answers, the learning algorithms will do the rest. Asking for interpretability as a condition for real world usages is undermining the foundations of the whole field. If the trained model has good performances and it’s not interpretable we are probably on the right track; if it’s interpretable (and the explanation is understandable and replicable) why loosing weeks and GPU power? Just write some if-else clauses.

Source: If it’s interpretable it’s pretty much useless. – Massimo Belloni – Medium

Aurélien Géron 所著《Scikit-Learn与TensorFlow机器学习实用指南》附录有份机器学习项目的检查清单，非常实用。
故翻译如下。

描述问题并观察大局（the big picture）

1. 用商业术语描述目标。
2. 你的解决方案要如何运用？
3. 目前的解决方案或变通方法是什么？
4. 你要如何表述这个问题（监督/非监督学习，在线/离线学习等）？
5. 怎样衡量性能？
6. 性能指标与商业目标一致吗？
7. 达到商业目标的最低性能是什么？
8. 有没有类似的问题？你有可重复使用的经验或工具吗？
9. 是否有专业人士的支持？
10. 你如何手工解决这个问题？
11. 列出你（或其他人）到目前为止的假设。
12. 尽量核实假设。