Pachyderm Case Studies Risk Thinking: How Riskthinking.AI Uses Machine Learning to Bring Certainty to an Uncertain World

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	Risk Thinking: How Riskthinking.AI Uses Machine Learning to Bring Certainty to an Uncertain World Pachyderm

Risk Thinking: How Riskthinking.AI Uses Machine Learning to Bring Certainty to an Uncertain World

Pachyderm

Technology Category	Analytics & Modeling - Machine Learning Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries	Cities & Municipalities Healthcare & Hospitals
Applicable Functions	Business Operation
Use Cases	Predictive Maintenance Public Transportation Management
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

Cities & Municipalities

Healthcare & Hospitals

Applicable Functions

Business Operation

Use Cases

Predictive Maintenance

Public Transportation Management

Services

Data Science Services

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 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.

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.

Impact #1	Riskthinking.AI was able to create the CovidWisdom app, a dashboard that tracked the different approaches to shutting down during the pandemic.
Impact #2	Their models showed the economic challenges of the pandemic in bold detail, allowing city and state officials to visualize what would happen if they implemented no restrictions at all, all the way to complete lockdowns.
Impact #3	They were able to model whether targeted lockdowns of specific types of places and events could work, allowing world leaders to see what would happen if they decided to shut down concerts, bars and gyms but nothing else or if they targeted only concert halls and bars, as well as dozens of other variations.

Impact #1

Riskthinking.AI was able to create the CovidWisdom app, a dashboard that tracked the different approaches to shutting down during the pandemic.

Impact #2

Their models showed the economic challenges of the pandemic in bold detail, allowing city and state officials to visualize what would happen if they implemented no restrictions at all, all the way to complete lockdowns.

Impact #3

They were able to model whether targeted lockdowns of specific types of places and events could work, allowing world leaders to see what would happen if they decided to shut down concerts, bars and gyms but nothing else or if they targeted only concert halls and bars, as well as dozens of other variations.

Benefit #1	The use of Pachyderm allowed Riskthinking.AI’s 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.
Benefit #2	The platform enabled the team to run multiple models simultaneously and visualize backtesting results with easy to understand images.
Benefit #3	The best performing model got automatically pushed to the application for the current day, improving efficiency and accuracy.

Benefit #1

The use of Pachyderm allowed Riskthinking.AI’s 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.

Benefit #2

The platform enabled the team to run multiple models simultaneously and visualize backtesting results with easy to understand images.

Benefit #3

The best performing model got automatically pushed to the application for the current day, improving efficiency and accuracy.

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Overview

Risk Thinking: How Riskthinking.AI Uses Machine Learning to Bring Certainty to an Uncertain World

Operational Impact

Quantitative Benefit