Our Data scientists work to tailor the search exactly to your needs, selecting the correct Machine Learning tool. Then that ML tool uses a UnifyCloud interactive training process to manually identify validation sets.
To train the tool, our Data Scientists create Test sets. For example to train the Machine Learning (ML) Tool, we create model using our proprietary techniques. For example, for on Global 100 customer we started by selecting ‘documents’ for training, for example using some as ‘objectionable’ samples, and others as ‘non objectionable’ samples. Other samples are held in reserve to be used for the Machine Learning training.
- UnifyCloud then creates Modeling Files, creating analytical models to run on existing data.
- Advanced modeling/analytic services entails extracting actionable useful information from the data.
- It encompasses statistical modeling, data mining, text mining, and knowledge discovery.
- UnifyCloud does this through the application of computer technology, operation research, and statistics to solve problems.
After repeating this process until the ML tool is trained to a high efficiency, we run the ML Tool on additional data, randomly selecting from the outputs a statistically valid set of documents to analysis the efficiency of the ML Tool. From these random documents we determine if the Machine Learning tool is working properly, and provide the additional training and feedback to the ML Tool.
We continue with the iterative training until the success rate is optimized and acceptable, and the ML tool is able to operate. Ongoing training validation is recommended as a best practice.
Some of our successful applications include:
- create recommendations to help your users find things
- automatically organize content by subject
- show connections based on similarity using either content or click history
- strengthen search with users’ past click behavior
- predict the future value of something based on history
- Identifying Security Risks