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Data analytics plays a crucial role in artificial intelligence (AI). AI systems are typically based on machine learning, in which algorithms learn from large amounts of data to identify patterns, relationships and rules. Data analytics makes it possible to extract relevant information from existing data and make it useful for AI models.
Here are some important aspects of how data analytics is used in AI:
Data sourcing and preprocessing:
Data analytics involves sourcing, cleaning, and transforming raw data to prepare it for processing by AI models. This step is critical because the quality and representativeness of the data has a major impact on the performance of the AI.
Feature extraction: data analysis helps identify relevant features or attributes in the data that are important for AI model learning. By applying statistical methods or other techniques, relevant information can be extracted from the data.
Training data for machine learning: data analytics allows large amounts of training data to be analyzed and processed to train AI models. This includes labeling data to show the AI algorithms what results are expected.
Model selection and validation: data analysis helps evaluate and select appropriate models for specific AI tasks. By analyzing the performance of different models against validation data, the best models can be identified.
Model selection and validation.
Monitoring and adaptation: data analytics also plays an important role in monitoring and adapting AI models on the fly. By analyzing real-time data, the performance of the model can be evaluated and adjusted as needed.
Overall, data analytics is an essential part of the AI lifecycle. It helps AI gather and process relevant information to enable accurate predictions, decisions, or other tasks. Without thorough data analysis, many AI applications would not be able to deliver the desired results.