Learn how to answer the question of what is a key consideration regarding data quality in AI implementation. Find out why data’s role in training and fine-tuning Salesforce AI models is the correct answer and what are the implications of data quality.
Table of Contents
Question
What is a Key consideration regarding data quality in AI implementation?
A.Techniques from customizing AI features in Salesforce
B.Data’s role in training and fine-tuning Salesforce AI models
C.Integration process of AI models with Salesforce workflows
Answer
B.Data’s role in training and fine-tuning Salesforce AI models
Explanation
Data quality is a key consideration regarding AI implementation because it affects the performance, accuracy, and reliability of AI models. Data quality refers to how well the data reflects the real-world phenomena or objects that it represents, and how fit it is for the intended purpose. Data quality can be measured by several dimensions, such as accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose.
Data plays a crucial role in training and fine-tuning Salesforce AI models, such as Einstein Prediction Builder, Einstein Discovery, and Einstein Vision and Language. These models use machine learning algorithms to learn from data and make predictions or recommendations based on the patterns and relationships they discover in the data. These models can also improve themselves over time by learning from new data and feedback.
However, if the data used to train or validate Salesforce AI models is of poor quality, such as containing errors, inconsistencies, biases, or missing values, the models may produce flawed or misleading results that can cause poor performance and failure. Therefore, ensuring data quality is essential for realizing the full potential of Salesforce AI models in delivering value, driving innovation, and ensuring ethical outcomes.
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