The AI Model development lifecycle - Deepstash

The AI Model development lifecycle

AI model development involves multiple stages that interconnect to each other.

  1. Identify the business problem. Instead of asking how to improve your artificial intelligence, ask how to improve your business.
  2. Identify and collect data. Identifying the correct data is vital to ensure model accuracy and relevance.
  3. Preparing the data.
  4. Model building and training.
  5. Model testing. The model is trained and tuned using the training and validation data sets.
  6. Model deployment. Once the model is tested with different datasets, you will have to validate model performance using the parameters from Step 1.

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Analyse if the KPIs and the business objective of the model are achieved. If the parameters are not met, consider changing the model or improving the quality and quantity of the data.

Before deployment:

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The ML workflow

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To develop and manage a production-ready model, you must work through the following stages:

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Phase 4: Nail the Business Model

  • Your business model is the map of how you create value and deliver it to customers. In this phase, we will conduct financial analysis to verify business viability, launch your product and go-to-market strategy, and then develop a dashboard to monitor your progress forward.

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