Machine Learning uses Neural network models and allows learning of information from the data available and use that for further classifying or predicting real-time data. Either Supervised learning or unsupervised learning strategies can be followed. These are typically resource intensive and need powerful systems. With the advent of embedded ML and powerful embedded systems, it is possible to run them at the edge, infer and act on the data as soon as it is acquired. Embien has been actively working on the Embedded ML development of algorithms and technologies.
TensorFlow, PyTorch, Keras and Caffe are some of the embedded machine learning frameworks that we leverage for customers and making them available transparently. We take advantage of the GPUs, FPGAs and NLPs present in the system and improve the performance.
Perform pre-processing on data, annotate them using manual easy-to-use tools, train the models, validate them on offline/cloud or online models
Not all data are the same with each of them requiring different treatment. Our experts build one or tune existing models to meet the needs exactly
With continuous learning, our implementation can extract anomalies, perform re-training, and update models without affecting the real-time performance
Our team understands the business needs, identify the model, train it on data, validate it before deploying the same. The user interface are kept minimal abstracting the end-user from the complexities.
Some of the models we have hands-on expertise on course of Embedded ML development are.
Our team can help in annotating the data as well to train the models quickly and deploy them on Renesas, Xilinx, TI and Nvidia edge computing platforms.