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Risk Thinking: How Riskthinking.AI Uses Machine Learning to Bring Certainty to an Uncertain World
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Healthcare & Hospitals
- Cities & Municipalities
Applicable Functions
- Business Operation
Use Cases
- Predictive Maintenance
- Public Transportation Management
Services
- Data Science Services
The Challenge
Riskthinking.AI, a company specializing in measuring the financial risk of climate change, was in the early phases of ramping up their internal AI infrastructure when they took on the CovidWisdom project. The project was a response to a call from the Canadian government to assess the economic impact of major pandemic policies. The challenge was to predict the best way to implement societal-level responses like lockdowns with the minimum amount of damage to daily life and the economy. However, the team realized they had experts in predicting the future but not in building AI architecture. They had data scientists working on laptops, pulling and pushing data over VPNs to remote work spots, and even building their own Docker containers. They needed to move from ad hoc to MLOps.
About The Customer
Riskthinking.AI is a company that specializes in measuring the financial risk of climate change. They work with companies and governments to help them make the best decisions when it comes to uncertain futures. For example, they might help an electric company decide whether to rebuild transformers in the same spots that caused forest fires in the past or to put them in a different configuration to reduce the chance of starting another fire in the future. They also help companies figure out how quickly they can ramp up a solar farm and where they should put it. However, they realized early on that while they had experts in predicting the future, they did not have expertise in building AI architecture.
The Solution
Riskthinking.AI decided to use Pachyderm, a platform that allowed their scientists to focus on the complexity of models rather than the complexity of figuring out which model was trained on which version of the dataset. It gave them the foundation to work with data and deploy any ML tool they wanted inside their machine learning loop. As Riskthinking.AI’s data scientists got more comfortable with pachctl and the command line, they used Pachyderm to run multiple models simultaneously and to visualize backtesting results with easy to understand images. The best performing model got automatically pushed to the application for the current day. The visualizations were not just for the data science team. They could also share their progress with non-technical or less technical stakeholders.
Operational Impact
Quantitative Benefit
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