SparklingSoDA maintains the best performance possible through repetitive relearning and experimentation, to solve problems that clients have and to continuously improve itself.
Through an AI model lifecycle that quickly learns and reflects changes, SparklingSoDA assetizes its learning results.
Maintains optimal performance through the learning/provision/evaluation pipeline, based on continuous experiments.
Provides packages for model development in upload/mirroring types, as well as learning environments for models and users (groups).
Users have individual experiment spaces and multiple containers that creates and environment conducive to team work.
Performance maximization through managing existing models (depending on the system conditions) or combining other solutions.
SparklingSoDA enables the management of every stage of pipeline – log/history/tained model. From a long-term perspective,
it increased a company’s AI capacity.
SparklingSoDA creates individual learning experiment environments suited for project model development, which enables model management at the individual/group/team-level.
Project Created
Experiment/Trial
SparklingSoDA does not stop at applying models created through training but continues to evaluate and retrain for even better performance.
SparklingSoDA supports the monitoring of server resources used in a model’s training performance and development.
Graphs to compare the performance of individual/multiple models
Resource pool management for each project Ability to register, edit, delete CPU/Memory/GPU resources
Dashboard support using metrics resource graphs linked with open source monitoring tools CPU / Memory / GPU usage monitoring
SparklingSoDA increases corporate advantage by providing a high-quality operation environment with relatively low costs, for companies that are thinking about implementing AI technology or those that want to use existing AI systems more efficiently.
Credit evaluation / Fraudulent transaction detection / Risk assessment for insurance policy holder / Determination of insurance payment
Demand forecasting / Determination of price policies / Supply chain management / Quality assurance / Security management
VOC data analysis / Product recommendation/ Management of customer inflow / Customer purchase route analysis
Establishing an AI environment for Company A
What previously took one month took only 3 days to develop, test, distribute and operate.
Developing a recommendation model for Company B
Accuracy of the purchase frequency-to-recommendations ratio increased threefold
Algorithm-based recommendations
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