We support ocean related organizations with technology infrastructure and expertise from leading data science and oceans researchers to solve your data challenges.
Many organizations working in the oceans space do not have the opportunity to fully explore Artificial Intelligence and Machine Learning. DeepSense exists to help companies leverage technology and research to more easily use data to make decisions, optimize operations, and create new products, processes or services.
DeepSense projects come from ocean sectors including Energy, Food, Defence, Transportation, and Tourism.
Using data, we help companies:
- Develop commercially useful predictive models
- Design and test analytical prototypes and applications
- Automate processes
- Discover insights in company data
- Improve internal operations
Neural Networks
Prediction
Classification
Image Recognition
Machine Vision
Planning Schedule Optimization
Information Extraction
Our process
We work with companies with varying levels of data maturity. Whatever your current situation, every DeepSense project is customized to align with your unique business strategies and goals.
Our project process follows 4 stages:
- Review current opportunities
- Determine fit for Artificial Intelligence and Machine Learning
- Complete Artificial Intelligence/Machine Learning canvas to determine target predictions, learning required and method for evaluating success
- Create clearly defined and measurable objectives
- Determine project feasibility
- Review scientific literature
- Data review and discovery to ensure data quality, quantity and appropriateness for project
- Perform exploratory analysis to find patterns and relationships through data queries, data visualization, data mining and statistics
- Identify and acquire data, if required
- Pre-process data for Artificial Intelligence/Machine Learning
- Run experiments to develop a model using historical data
- Develop a proof of concept or viable code to optimize a process, product or operations
- Support with launching prototype model in production
- Assist in determining how best to integrate the model with existing applications
- Ensure adequate monitoring and viability once model is live