Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records. The data removed in this process is often referred to as “dirty data”. Data cleaning is a necessary process to protect data quality. Large businesses with extensive datasets or assets typically use automated tools and algorithms to detect such records and correct common errors (such as missing zip codes in customer records).
The most powerful big data circles have rigorous data cleanup tools and processes to ensure that data quality is protected and trust in datasets is high for all types of users.
Data replication is the process of moving data from one place to another, copying it, or storing data in more than one place at the same time.
LLaMA (Large Language Model Meta AI), Meta (eski adıyla Facebook) tarafından geliştirilmiş bir büyük dil modelidir.
In AI and machine learning projects, instead of processing raw data directly, it is necessary to make it more meaningful and processable. An important concept that comes into play at this point is Embedding.
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