From the course: AI Security and Responsible AI Practices by Pearson

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Understanding the AI data lifecycle management

Understanding the AI data lifecycle management

From the course: AI Security and Responsible AI Practices by Pearson

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Understanding the AI data lifecycle management

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In this section, we want to cover the entire data lifecycle for artificial intelligence systems. The step number one in AI data lifecycle management is to gather raw data from various different sources. Then, we move to the step number two, when we clean and we normalize and we transform the data before we apply it to the algorithm. Step number three is to store the data in databases or data lakes to manage access and to manage security. We might need that to train other models in the future or just for regulations to comply with regulations to prove where did we get the data and what type of data did we get. In step number four, we use algorithms to analyze and process the data for insights. In step number five, then we start to create the AI models and to train them with the processed data. So all of those steps up to step number five are happening before we even start developing the model. Step number six is when we assess the performance of the model. Now we spoke about evaluation…

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