March 2021
Intermediate to advanced
73 pages
1h 37m
English
Content preview from Operationalizing AI
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Chapter 4. Stages of the AI Life Cycle
Businesses need a systematic approach to operationalize AI, one that takes into consideration the end-to-end data science and AI life cycle. The AI life cycle consists of a sequence of stages that brings together the personas we discussed earlier into a collaborative process. These stages are generally defined as follows:
- Scope
-
How can business stakeholders and data science teams scope the deluge of use case requests and decide on priority? What role do key performance indicators (KPIs) play in defining successful business outcomes? As we’ll discuss in the next chapter, design thinking is a best practice that should take place during the Scope stage of the AI life cycle.
- Understand
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How do the data provider, data governance, and data science teams work together on the datasets needed to implement a given use case?
- Build
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What tools and techniques are used by the data science team for data preparation, feature engineering, model training, and building?
- Deploy
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How can the data science and operations teams work together to deploy and track versions of models and make them available for scoring, whether online or batch?
- Manage and Trust
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How can the operations team ensure high performance via monitoring and retraining? How can data science, model validation/MRM, operations, and business teams work together to ensure trust and transparency by attending to bias detection, fairness, drift, explainability, and so on?
The AI life ...
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