Machine learning is one of the fastest growing fields of AI and presents immense opportunities for enterprise use cases however, there are number of challenges too.
Machine learning is data intensive and requires lot of training data upfront to train the model to get good accuracy. Also, if the data in production changes which does happen, model may not work as expected.
Hence it is imperative from the start to develop a robust, scalable and adaptive model which can continue giving expected results with minimal support in production.
Our engineers at Thinqe are experienced in developing production grade model using TensorFlow and Google ML APIs. We use MLFlow platform to manage machine learning lifecycle and IntelliFlow for automated defect management and quality gating. Some of the challenging work our engineers have upndertaken are -
Development of Tensorflow based unsupervised recommendation and scoring system for intelligent defect management
Development of supervised intelligent repository scanning system Machine learning has been very effective technology in narrow list of use cases but not that effective as a general framework which can be applied across the businesses Thinqe is investing in innovations to explore more enterprise business cases where machine learning can be utilized to give business value