Kogentix gives your enterprise the leading edge over competitors.
Our tools offer insights and predictions you haven’t even thought possible.
This lets you see game-changing opportunities you can not only act on, but that can be tweaked in the real-time
Building modern AI applications is far too difficult, leaving the promise of AI unfulfilled for most organizations.
Fortunately, that’s about to change.
Kogentix AMP makes it possible to build, manage, monitor, and deploy AI applications leveraging cost ctive, scalable open source software. Business leaders and data scientists can focus on using their data for superior results, not scripting and managing a wide variety of ever-changing tools. For data to have an impact, the entire data science process must be automated and operationalized.
Kogentix AMP goes well past models and predictions. Our technology delivers an end to end a solution that integrates and wrangles data from multiple sources, and builds machine learning models and operationalizes AI applications that will generate visible business results.
Out of box click and configure connections to acquire data from your enterprise systems ex. Teradata, SAS, Oracle etc cloud storages ex.S3, ADL etc and 3rd party external systems. AMP also provides access to various public datasets
Wrangle your data native to your platform. Automated identification of features and their feature behavior change tracking.
AMP out of the box helps you understand your data better, discover patterns , trends, correlations, hypothesis testing on full set of all variety of data. AMP also allows user to create custom slice and dice data and multilevel reporting within the same experience.
Enable experts and citizen data scientists to build powerful predictive tools. You can choose to execute a full feature drag and drop, code the interface, or use a combination of both
AMP integrates with the underneath platform security, Collaboration, authentication and authorization framework when existing. Also governance around auditability of action, and flow for approval before operationalising the applications is all built into AMP
AMP allows you to act on the model predictions or take actions based on business rules setup by domain experts through a drag and drop interface.
AMP out of the box provides lineage and traceability for each data structure, model, fit statistics. Now you can clearly see what is the accuracy of models and trace various feature stats and model performance and visualize to why certain apps are performing better or otherwise now
AMP allows full fledged functionality to build, train, validate, experiment models and out of box store each configuration and stats for each run. The user can now just click and visualize the performance and manage which configuration, trained model needs to be deployed
AMP validates multi-models, identifies best features, sets up A/B Testing to experiment, and picks the top performing one allowing enterprises to share and collaborate.
AMP out of box provides multiple models all distributed or parallelized on spark . The user can just click and select models for classification , clustering , regression, Time series, Graphs, Survival Analytics, Deep Learning . All stats and configuration are saved for each training and scoring runs. The user can transparently various stats, charts, and compare across models to identify the best model
AMP empower enterprises to deploy and operationalize models in real- time. Batch or rest API’s with inbuilt governance can be integrated with a few clicks.
AMP enables the user to deploy and operationalize models with just a few clicks. Now handing over a trained model logic to developer to develop in production grade distributed is a thing of past. Move models into production and operationalize in minutes in real time streaming, batch or Rest API’s
AMP allows model performance and accuracy to be tracked and monitored when deployed. Learn and improve based on feedback and recommend when the model needs to be recalibrated.
Hyper customizability and control results in boosted transparency and predictability over natively distributed models for classification, clustering, regression, time series, graphs, survival analysis and deep learning.
With AMP the model performance and accuracy can be tracked and monitored even after the model is deployed and predicting. AMP out the box can detect anomalies and recommend which alternate model will perform better and when the model needs to be recalibrated.
AMP enables enterprises to integrate existing module writing in Python, PySpark, R, Scala, or Java into the AMP data flow.
Enterprises can leverage their existing data lake investments on-premise or cloud . AMP runs on Spark and natively on your enterprise hadoop distributions or your cloud providers Amazon web services, Google cloud, Microsoft Azure