Machine Learning (ML) Strategy
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A Machine Learning (ML) Strategy is an AI strategy that focuses on machine learning technologies.
- 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.
- ...
- ...
- An Task-Specific ML Strategy, such as:
- Counter-Example(s):
- a Cloud-Computing Strategy (for cloud computing).
- an Edge-Computing Strategy (for edge computing).
- See: AI Strategy, Cloud-Computing Strategy.
References
2020
- 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. ...
2020
- https://cloud.withgoogle.com/build/data-analytics/new-report-6-steps-implementing-ml-strategy/
- QUOTE: Based on a survey of senior IT leaders as well as in-depth interviews with technology executives, the IDG guide outlines six basic steps to implementing an ML strategy. These include:
- Identifying use cases
- Choosing a well-understood problem
- Determining success metrics
- Coping with talent shortages
- Assessing infrastructure options
- Showcasing the business value of ML projects
- QUOTE: Based on a survey of senior IT leaders as well as in-depth interviews with technology executives, the IDG guide outlines six basic steps to implementing an ML strategy. These include: