Meeting the needs of data scientists and ML models
Over the past decade, the field of ML has significantly expanded, and the majority of larger organizations now have data science teams that use ML techniques to help drive the objectives of the organization.
Data scientists use advanced mathematical concepts to develop ML models that can be used in various ways, including the following:
- Identifying non-obvious patterns in data (based on the results of a blood test, what is the likelihood that this patient has a specific type of cancer?)
- Predicting future outcomes based on historical data (is this consumer, with these specific attributes, likely to default on their debt?)
- Extracting metadata from unstructured data (in this image of a person, are they smiling? Are they wearing sunglasses? Do they have a beard?)
Many types of ML approaches require large amounts of raw data to train the machine learning model (teaching the model about patterns in data). As such...
