Training a machine learning model is one thing, but managing and putting all those pieces together is a whole different story. Our listed features will combine machine learning with standard software engineering best practices.
The Salynt Analytic Development Accelerator (ADA) allows coders and non-coders to easily build data science workflows. The workflows are designed with adjustable parameters so coders can create advanced functions for unique customization.
Salynt provides Dynamic Prioritization as your project grows. Its rich GUI has features including ADA visualizations, execution progress monitoring, scheduling, and triggering.
Salynt provides a collaborative workspace for multiple users to share projects. Each Salynt project is versioned specifically for that project to include the ADA, data pipeline, and machine learning models associated with the project.
Salynt helps you visualize the machine learning data and model training pipeline. Using our platform, you can see the overall pipeline of the system.
Salynt's handling and loading/saving different datafiles is the first thing every machine learning engineer will encounter. Salynt out of the box supports commonly used data files (CSV ,XLSX, JSON, XML, HTML, Images, MP3/4 and much more).
Users can create new modules or use our pre-designed modules of algorithms to improve their model accuracy.
Salynt allows you to monitor variables in real-time. This is a key advantage for adjusting your model for faster efficiency. Once you have a fully functioning analytic, Salynt provides the deployment code and models to push your rapid analytic to production
Salynt can run on-premise or in the cloud - with supported instances on Amazon Web Services (AWS) and Google Cloud Platform (GCP).