What are the AI/ML data preparation steps?
In order to create a successful Artificial Intelligence or a Machine Learning data preparation model, every data scientist must be able to train, test and validate the model before deployment to production.
For a data scientist to prepare data to go to production, they must follow the following steps;
Data collection
Data collection is the preliminary step that addresses common data challenges, which include;
- Parsing highly-nested data structures into tabular form, for pattern detection and easier scanning.
- Searching and identifying data from external repositories
A data scientist will need to make sure that the data preparation model they are considering is capable of combining multiple files into a single input. They also need to have a contingency plan set up so as to overcome any challenge arising from sampling and bias in a data set or the AI/ML model.
Data Profiling
This is the second step after data is collected. Here, the data is assessed on its condition, to identify trends, exceptions, outliers, missing, incorrect and inconsistent information.
Data exploration and profiling is an important step because it identifies any biases in the source data, which informs all the model’s findings.
Biased data could potentially alter your model’s findings, an entire data set or a partial data set.
Data formatting
The third step involves formatting the unbiased data in the best way possible that fits your AI/ML model. For instance, data that is aggregated from different sources and is updated manually may contain inconsistencies. Formatting this data to remove errors, makes it consistent for use in the model.
Data quality improvement
In this step, start by having a methodology for managing mistaken data, missing qualities, outrageous qualities, and exceptions in your data. Self-administration data readiness instruments can help on the off chance that they have insightful offices worked in to help coordinate data credits from divergent datasets to join them astutely.
For consistent factors, make a point to utilize histograms to survey the dissemination of your data and diminish the skewness. Make certain to look at records outside an acknowledged scope of significant worth. This “anomaly” could be a contributing blunder, or it could be a genuine and important outcome that could illuminate future occasions as copy or comparable qualities could convey a similar data and ought to be disposed of. Likewise, take care before consequently erasing all records with a missing worth, as such a large number of cancellations could slant your data set to no longer reflect true circumstances.
Feature engineering
Feature engineering is the step that involves the use of domain knowledge in extracting features from raw data through different data mining techniques.
Splitting data into preparing and assessment sets
The last advance is to part your data into two sets; one for preparing your calculation, and another for assessment purposes. Make certain to choose non-covering subsets of your data for the preparation and assessment sets so as to guarantee legitimate testing. Put resources into devices that give forming and listing of your unique source just as your readied data for contribution to AI calculations, and the ancestry between them. Along these lines, you can follow the result of your expectations back to the information data to refine and enhance your models after some time.
