this repo has no description
📝 Enhance clarity
+2
-2
+2
-2
Machine Learning.md
+2
-2
Machine Learning.md
···
2
2
3
3
## Production Project Checklist
4
4
5
-
1. Frame the problem. Define a clear and concise objective with clear metrics. [Write it as a design doc](https://applyingml.com/resources/ml-design-docs/).
6
-
1. Get the data. Make the data tidy. Machine learning models are only as reliable as the data used to train them. [The data matters more than the model](https://twitter.com/beeonaposy/status/1353735905962577920). Data matters more than the model.
5
+
1. Frame the problem. Define a clear and concise objective with clear metrics. [Write it as a design doc](https://applyingml.com/resources/ml-design-docs/). To know "what it is good enough" you have to collect and annotate more data than most people and organizations want to do.
6
+
1. Get the data. Make the data tidy. Machine learning models are only as reliable as the data used to train them. [The data matters more than the model](https://twitter.com/beeonaposy/status/1353735905962577920). Data matters more than the model. [The main bottleneck is collecting enough high quality data and getting it properly annotated and verified](https://news.ycombinator.com/item?id=45875618). Then doing proper evals with humans in the loop to get it right.
7
7
1. Explore the data. Verify any assumptions. Garbage in, garbage out.
8
8
1. Create a model. [Start with the simplest model!](https://developers.google.com/machine-learning/guides/rules-of-ml/). That will be the [baseline model](https://blog.insightdatascience.com/always-start-with-a-stupid-model-no-exceptions-3a22314b9aaa). Evaluate the model with the defined metric.
9
9
1. Make sure everything works end to end. _You design it, you train it, you deploy it_. [Deploy the model quickly](https://nlathia.github.io/2019/08/Machine-learning-faster.html) and automatically. Add a clear description of the model. [Monitor model performance in production](https://youtu.be/hqxQO7MoQIE).