Generate & store data from multiple sources in cloud or on premises.
Bring structured & un-structured in one toolkit.
Find relevant data from knowledge database.
Use necessary data based on the application
Data mugging (clean and prepare data).
Use relevant open source software for cleaning data for processing .
Development of ML/DL models programmatically or visually using open source packages.
Leverage of pre-trained models to meet requirements.
Train models to its accuracy.
Deploy model to the specific area and scale automatically to any case.
Continuously monitor the performance of the model and automatically trigger re-training and redeployment of models whenever necessary.
Use AI combined with high res cameras to pick out minute details and defects more reliably than the human eye. Integrate your systems with a cloud-based data processing framework and instantly flag a defect and automatically coordinate a response.
Input your design goals, along with parameters such as materials, manufacturing methods, and cost constraints and receive all possible permutations of a solution and design alternatives. The system tests and learns from each iteration and decides what works and what doesn’t.
Define the optimal conditions for equipment, systems and processes to predict possibilities of failure or need for maintenance. Use Machine Learning to identify patterns in predictions and fix the root cause of issues.
Reduce redundancies and costs due to delayed communication. Data collected on one production line can be interpreted and shared with other branches to automate material provision, maintenance and other previously manual undertakings.
Use NLP (Natural Language Processing) and Text Analytics to identify customer sentiment online and through feedback forms and directly feed the information to quality and production teams to identify areas of improvement.
Optimize product composition and production techniques using deep learning to minimize costs and improve output. Use demand forecasting models for accurate demand driven production.
Range of domain knowledge to teach relevant software and build custom models.
Experience in the field will help improve end-user experience gag Modeler.
Runs experimental trials to build models to meet project requirements.
Uses techniques on Data analytics, ML/DL model and to furthur develop Al models with help from domain expert.
Works on establishing the infrastructure for the data, mainly related to pre-processing of data for analysis.
Works closely with Modeler to transform models in to production quality systems.
PoC's which includes
gathering the content and model training with the application.
Focus on building the platform for the end-user.
Responsible for transfer of requirements to products.
Augmented and Virtual