Topic: Gathering and curation of datasets (Read 96 times)
Agreed. Lots of machine learning projects require big datasets, but those generally aren't available (either in quantity, or quality).
Gathering and quality control of structured data are the main hassles of machine learning engineers. Gathering methods generally produce a single set of structured data (this can be mitigated by smart gathering methods, classifications and some software features)
Examples of datasets gathering method:
● Bots: Automated script crawling the web of database to “mine” data;
● Thirds parties: External third parties which will provide inputs , that are aggregated together to form new set of structured data;
● Bulk buys: External data processors or open sources datasets.
Examples of datasets gathering method:
● Bots: Automated script crawling the web of database to “mine” data;
● Thirds parties: External third parties which will provide inputs , that are aggregated together to form new set of structured data;
● Bulk buys: External data processors or open sources datasets.
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