A few changes in prioritization can have a huge effect on a machine learning team's productivity.

Google

Machine learning strategy is the practice of choosing useful directions for improving a machine learning application. It uses clues from the problem to decide what is useful to try and what is not useful to try.

Machine Learning Strategy

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

When a cat detector is not accurate enough, possible improvement ideas include collecting more cat pictures, collecting a more diverse training set, training longer, trying a bigger or smaller neural network, adding regularization, or changing the neural network architecture.

Cat Detector Improvement Ideas

Choosing well among possible machine learning improvement directions can lead a project toward success, while choosing poorly can waste months. Reading problem clues can save months or years of development time.

Choosing Machine Learning Improvement Directions Can Save or Waste Months

Most machine learning problems contain clues about which ideas are useful to try and which ideas are not useful to try. Learning to read these clues can save months or years of development time.

Machine Learning Problems Leave Strategy Clues

A goal of machine learning strategy is to help set technical direction for a machine learning project.

Setting Technical Direction for a Machine Learning Project

Prioritization Changes Can Affect Machine Learning Team Productivity

Machine Learning Yearning starts with general strategies useful for both traditional learning algorithms and neural networks, then builds toward modern strategies for deep learning systems.

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