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💡 This page documents the development process engineers can use as a guideline for their projects. It’s pretty close.
P.S. .. Did you arrive here from theDonjon.tech? Here, let me help you get back there.
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The 6 steps in a standard machine learning life cycle:
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Planning
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Data Preparation
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Model Engineering
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Model Evaluation
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Model Deployment
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Monitoring an Maintenance
1. Planning
- Mayka da lista!
- Define the problem statement and goals of the project
- Identify the data sources and determine what data is needed
- Determine the feasibility of the project given the available resources and time constraints
- Define the success criteria and metrics for evaluating the model's performance
- Choose the appropriate machine learning algorithms and techniques based on the problem being solved
- Determine the infrastructure and tools needed to support the project
- Create a project timeline and allocate resources accordingly