Machine Learning (ML) Strategy

Context:
- It can include identification of ML-Supporting Use Cases.
- It can include choosing a well-understood problem
- It can include determining success metrics
- It can include coping with talent shortages
- It can include assessing infrastructure options
- It can include showcasing the business value of ML projects
- …
Example(s):
- An Task-Specific ML Strategy, such as:
  - a Recommender System Strategy to personalize store recommendations.
  - a Fraud System Strategy for cybersecurity.
  - ...
- An ML Technology-Specific Strategy, such as:
  - an ML Development Platform Strategy, that touches on Cloud-based ML platforms.
  - a Deep Learning Strategy, that touches on Tensorflow vs Pytorch.
  - an LLM Strategy, that touches on open-source LLMs and LLM fine-tuning.
  - ...
- A Company-Specific ML Strategy, such as:
  - Amazon's ML Strategy, focusing on personalizing shopping experiences and optimizing logistics.
  - Netflix's Recommendation Engine Strategy, enhancing content discovery and viewer satisfaction.
  - Spotify's Music Recommendation Strategy, tailoring playlists to individual user preferences.
  - ...
- ...
Counter-Example(s):
- a Cloud-Computing Strategy (for cloud computing).
- an Edge-Computing Strategy (for edge computing).
See: AI Strategy, Cloud-Computing Strategy.

References

https://altexsoft.com/blog/datascience/machine-learning-strategy-7-steps/
- QUOTE: ... There are generally two types of companies that engage in machine learning: those that build applications with a trained ML model inside as their core business proposition and those that apply ML to enhance existing business workflows. ...