What are the three steps in data preparation?

Anton Knight
Anton KnightAnswered

Raw data must be cleaned and formatted before it can be used in analytical processes.  Exploring and visualizing raw data after it has been collected, cleaned, and labeled to make it usable by ML algorithms are also important processes. In machine learning projects, data preparation and processing consumes as much as 80% of the total time. Optimizing this procedure requires the use of specialist data preparation tools.

ML is powered by data. While not easy, using this information to reimagine your company is critical to ensuring your continued viability. Data-driven decision makers who can swiftly adjust to the unexpected and seize new possibilities will prevail.

It is important to implement data preparation techniques and acquire the proper data before moving on to cleaning, labeling, validation, and visualization.

  • Collect. As the name implies, Data Collection is the process of accumulating all the information required for machine learning. The fact that information may be found anywhere from local hard drives to the cloud, databases, and hardware makes data collecting time consuming. It is sometimes difficult to link to several data sources. There is a lot of information to go through because data volumes grow at an exponential rate. There is also a large variation in the data’s format and nature depending on its origin. For instance, combining video data with tabular data might be difficult.
  • Clean. When data is cleaned, mistakes are fixed and information gaps are filled up. After you’ve gotten your data cleaned up, you’ll want to format it so it’s easy to read and work with. Date and currency formats, naming standards, and the conversion of values and units of measure may all be modified in this way to ensure uniformity.
  • Label. For a machine learning model to make sense of raw data, it must be identified and given one or more meaningful and useful labels. Labels might specify whether or not a photograph shows a bird or an automobile, which words were said during an interview, or whether or not an anomaly was spotted on an X-ray. NLP, computer vision, and audio recognition are just a few of the applications where labeling data is essential.
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