Which Database for your AI Platform?
Kinetica Aids Model Development and Model Deployment
Features are often derived from a variety of data sources, such as sensor data, machine data, video, and traditional transactions and log data. All of this should be stored in a data platform that can effectively support the generation.
With it's rich time-series and spatial capabilities, Kinetica is able to power more sophisticated features and hence increase the prediction power of ML models Kinetica can also store the results of feature extraction algorithms, such as the movement of objects derived from drone or closed circuit tv cameras.
Inference & Operationalization
Inference is the process by which a trained machine learning model makes predictions on new data. This typically involves serving the model through an API or other service that can accept requests and return predictions.
For more advanced ML models, Kinetica can store intermediate results or caches to improve the performance of the inference service. For example, Kinetica can continuously update and store the results of expensive computations every 10 seconds so that they do not need to be recomputed when predictions are requested 100 times every second.
Features You'll Need
Kinetica Architecture »
Data Processing Capabilities
Time-Series & Spatial Analytics »
High Cardinality Joins
The Power of Vectorization »
High Speed Read/Writes
Do More with Less »
Model Management in SQL
Model Management with SQL »
Machine Learning Functions in Kinetica
PredictionThe PREDICT table function will predict the values of the dependent variables that correspond to a given column of independent variables, using a given base table containing "historical" values of each. This table will be used as the basis to calculate the prediction.
SELECT * FROM TABLE ( PREDICT ( HISTORY_TABLE => INPUT_TABLE(example.ticket_prices), X_COLUMN => 'year', Y_COLUMN => 'cost', PREDICT_ON_TABLE => INPUT_TABLE(example.future_years), PREDICT_ON_COLUMN => 'year' ) )
Outlier DetectionThe OUTLIERS table function will calculate the outliers in a given data set, based on a specified calculation type, threshold, and partition column. The partition column allows the data to be segmented into subsets, one per unique partition column value, and have the outliers for each subset calculated & determined independently from other subsets.
SELECT * FROM TABLE ( OUTLIERS ( DATA_TABLE => INPUT_TABLE(example.employee), DATA_COLUMN => 'salary', THRESHOLD_LOW => -1, THRESHOLD_HIGH => 1 ) )
Book a Demo!
The best way to appreciate the possibilities that Kinetica brings to high-performance real-time analytics is to see it in action.
Contact us, and we'll give you a tour of Kinetica. We can also help you get started using it with your own data, your own schemas and your own queries.