Confidence in your model requires confidence in your training data.
Artificial Intelligence is only as good as the data used to train it. Alegion addresses the common challenges of building large-scale, custom training data sets.
Step 1: Data Collection
The first step in building quality supervisory data sets is collecting a large enough sample to represent both the base case and the edge cases of the model. This data may not be easily accessible, and collecting it may require humans to search, gather, scrape, sort, parse, and interpret information from various sources. Alegion tackles this effort by leveraging an army of on-demand workers to divide and conquer huge data extraction efforts. By utilizing Alegion, you can collect data from anywhere at any scale.
Step 2: Data Validation and Normalization
Once the data is collected, the easy part is over. The data must be validated and normalized to ensure the model receives consistent quality data. This step is typically done with a blend of automated functions and human judgement. Alegion uses a two step process to accomplish this. First, the data is passed through a series of algorithms to remove duplicate records and map the key data elements into a standard schema. Unfortunately, this usually only gets us about 60% of the way there. After the machines are done, the records are individually routed to pre-qualified workers who standardize the data according to the training rubric provided. If the data is missing, the record can be escalated to a research worker to identify missing fields and insert them into the data. The result is a single data set that only contains valid records in a standardized format.
Step 3: Data Structure and Metadata
The last step in producing a quality Training Data Set is appending additional metadata to add value to the data. Each record is presented to one or more workers to append record specific contextual metadata to enrich the initial data set. After the data is appended, a second worker immediately reviews the work to ensure accuracy and accountability. The metadata layer possibilities are as broad as your imagination. Here are just a few of the metadata layers we can append to your training data sets:
- Data Categorization
- Bounding Boxes
- Image Tagging
- Video Tagging & Time Stamping
- Sentiment Analysis
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