ANNHUB is a powerful, intuitive platform that allows engineers, educators, and system integrators to design, train, and deploy machine learning models — without writing a single line of code.
ANNHUB simplifies every step of the machine learning workflow. Whether you're building a predictive maintenance model, classifying sensor data, or analyzing event streams, ANNHUB lets you focus on outcomes — not algorithms.
No-Code, Engineer-Friendly Workflow
Build, train, and evaluate ML models using real-world data without coding or AI expertise, cutting down reliance on external AI consultants or complex toolchains.
LabVIEW-Ready Machine Learning
Models can be exported and integrated directly into LabVIEW systems, making it easy to embed ML capabilities into your test, measurement, or automation setups.
Secure, On-Premise Training
All training runs locally, ensuring full control over your data and model IP — ideal for organizations with strict privacy, security, or compliance requirements.
How It Works
ANNHUB supports datasets in comma-separated values (CSV) format. The outputs are identified by keywords "output, target, class." Each row in the csv file is equivalent to a data sample.
Key Features
Advanced algorithms
ANNHUB supports a variety of advanced algorithms that can be used to optimize the training speed and convergence rate of machine learning models.
✅ Scaled Conjugate Gradient (SCG): A gradient-based optimization algorithm that is more efficient than traditional gradient descent algorithms.
✅ Levenberg Marquardt (LM): A hybrid algorithm that combines the advantages of gradient descent and Gauss-Newton methods.
✅ Quasi-Newton: A family of algorithms that use an approximation to the Hessian matrix to improve the convergence rate of gradient descent.
✅ Bayesian Regularization: A method of regularizing machine learning models by adding a penalty term to the loss function that penalizes the complexity of the model.
Design assistant
ANNHUB can auto-suggest neural network architectures for performance optimization. This means that you can simply upload your data set, and ANNHUB will recommend the best neural network architecture for your specific task. This can save you a lot of time and effort in designing an AI model.
Flexible model testing
ANNHUB supports a variety of model evaluation metrics to test the performance of your model before final deployment., including:
✅ ROC curves: A graphical plot of the true positive rate (TPR) against the false positive rate (FPR), used to evaluate the performance of binary classification models.
✅ Confusion matrix: Shows the number of true positives, false positives, true negatives, and false negatives.
✅ Performance metrics: Quantify the performance of a machine learning model by common metrics include accuracy, precision, recall, and F1 score.
✅ Regression curves: Evaluate the performance of regression models.
Deploy model to LabVIEW
ANNHUB allows you to export your models in .zip format. The zip file contains all the necessary information, such as the model's architecture, weights, and biases, to deploy in LabVIEW using ANSDL
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