Healthcare is one of the expensive and critical domain compared to other domains like, retail, manufacturing, banking etc. Each individual…
Today many businesses rely on Machine Learning to derive patterns from data, predicting customer behavior, taking decisions and performing actions without human interventions. VOLANSYS Artificial Intelligence (AI) and Machine Learning (ML) services helps organizations to develop custom solutions based on proprietary or open source algorithms/frameworks that process data and run sophisticated algorithms on Cloud and Edge. This enables faster decision making, increased productivity, business process automation, and faster anomaly detection.
Machine Learning Services
Our team can create a Machine Learning Model based on your business requirements. We can build and train models using supervised (classification & regression-based), unsupervised (clustering & association based) and reinforcement (reaction to an environment based) learning. We also provide on-device inference using TensorFlow Lite model. To test the performance of Machine Learning models, we use Cross Validation, RSS, RSME, MSE, Log-loss, F-measure, Precision-Recall, etc. to ensure the best performance of models.
We optimize the model for maximum accuracy. For optimization, we use hyper-parameter 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 (i.e. neural network weights) from the training-time 32-bit floating-point representations into smaller and efficient 8-bit integer ones using TensorFlow Lite conversion tool.
Deploying models is the key to making them useful. Our team will help deploy and serve models in a structured way on the cloud using platforms like TensorFlow, Michelangelo, Amazon ML, Open CV, etc. For mobiles and embedded devices (Edge), a TensorFlow model is converted into a compressed flat buffer with TensorFlow Lite converter and then deployed on it.
Unlike traditional software that is pre-determined based on different inputs, ML models need continuous quality assurance. Our team of quality experts help in testing ML models from time-to-time by assuring the quality of data (used for training the model), features and ML algorithms for accurate model performance. They also have hands-on experience on testing tools like TensorBoard, What-If, ML Perf, TensorFlow Playground, etc. to understand, debug and optimize TensorFlow programs.
Machine Learning Process
Fig1: ML Process
Industries using AI/ML
Many industries working with large amounts of data have recognized the value of Machine Learning technology. By collecting insights from this data, organizations are able to work more efficiently and gain an advantage over competitors.
Fig2: AI/ML Applications
AI/ML Technology Expertise
Data Source: Audio, Video, Social Media, RAW, TXT, XLS, etc.
Frameworks: Spark MLlib, TensorFlow (NumPy, SciPy, Caffe, Keras), Scikit-learn, Theano, Torch, OpenCV, Pandas, Matplotlib
Tools: Octave, Anaconda, TensorBoard, What-If, ML Perf, TensorFlow Playground
Platforms: Google Cloud Vision, Amazon, Azure
Database: MongoDB, CouchDB, Cassandra, Microsoft SQL Server, HBase, Oracle