- The NeoPulse Platform can accept training datasets from different sources.
- Using the in-built data viewer, the user can inspect the data, and identify the input and output columns.
- The dataset can be archived for auditing purposes.
Creating & Importing AI Models
- The visual model builder will solve most problems automatically with an auto block
- One can use the visual editor to drag and drop layers and get help connecting them
- Advanced users can write NML code directly and inspect it in the visual model builder
- Import AI models created with Keras, Tensorflow, Pytorch or Chainer
- Import pretrained models for retraining
Training & Evaluation of AI Models
- Users can choose a range of loss functions and metrics for evaluating models
- Users can choose data for validating models or let NeoPulse® choose
- NeoPulse® explainability features allow users to open ‘black box’ AI models and answer the question ‘why?’
- Using NeoPulse Manager, a user can evaluate the best model for deployment with a couple of clicks
- Deployment is simply a drag and drop operation – dragging a model to the right NeoPulse® Runtime cluster or NeoPulse® Runtime group will distribute the model to all the nodes in the group or cluster automatically
NeoPulse DevOps Features
- Monitor AI models in production
- Stage AI models for inference
- Group deployment targets for easier management
- Perform A/B testing to replace models in production
- Verify status of production environments
NeoPulse can solve hundreds of real-world problems across dozens of industries: industry prediction, preventive maintenance, protein synthesis, repurpose drugs, detecting threats, and much more! Customers can build state-of-the-art models in minutes unlocking millions of dollars of NPV for their businesses every year.
In this live demo, NeoPulse Manager built a state-of-the-art machine learning model using COVID data from Kaggle with very minimal effort, beating the highest model currently listed with an accuracy of 95.27.