Deploying models is the key to making them useful. Our team can help businesses to deploy and serve models in a structured manner on the cloud using platforms. For mobiles and embedded devices, a TensorFlow model is converted into a compressed flat buffer with a TensorFlow Lite converter and then deployed on it.

We optimize the model for maximum accuracy. For optimization, we use hyperparameter tuning, gradient descent, SGD, ensemble, and many more processes, to derive the best outcomes from the model. Also, we help compress models by lowering the precision of the parameters from the training-time 32-bit floating-point representations into smaller and efficient 8-bit integer ones.

Our team creates a Machine Learning Model based on your business requirements. We build and train models using supervised, unsupervised, and reinforcement learning. We also provide on-device inference. To test the performance of Machine Learning models, we use RSS, RSME, MSE, F-measure, Precision-Recall, among others to ensure the best performance of models.

Unlike traditional software that is pre-determined based on different inputs, ML models need continuous quality assurance. Our qualified team helps in testing ML models from time-to-time by assuring the quality of data, features, and ML algorithms for accurate model performance. They also have hands-on experience on various testing tools which helps the team to understand, debug, and optimize TensorFlow programs.