Forecasting model predicting the success of fishing trips
With more than 7 million users, Fishbrain is the world’s largest fishing app, connecting anglers and allowing technology to change the way people fish. Users can log catches, share photos, make comments and also find the right gear for maximizing the chance of success on the next fishing trip.
The Modulai team joined forces with the engineers at Fishbrain to develop and deploy a machine learning based service that predicts what fish an angler is likely to catch a given day anywhere in the world.
Catch data was collected and combined with various internal and external sources. Data pipelines were developed for processing data from internal and external API’s. Various cloud managed services (such as Amazon Sagemaker and Lambda functions) were utilized for efficient model training, tuning and serverless deployment. Development as well as deployment was mainly performed and orchestrated in Python and open source libraries.
Thanks to a successful forecasting model, Fishbrain remains strong as the leading fishing app and can better provide their users with accurate predictions that helps them daily in where and how they can catch what fish.
|
2022-12-13 13:15
File (PDF, Word, PPT, etc)
Executive Summary Fishbrain is an app that lets anglers log their catches, network with other enthusiasts, and find the best places to fish. Fishbrain worked with AWS Partner Network (APN) partner Modulai and used Amazon SageMaker to build the models that power a new version of BiteTime, its fishing forecast. Fishbrain is the social network for anglers. It’s free to use, but users can pay for advanced features, including BiteTime, a service that predicts where and when to fish for certain species. To make sure paying customers get the most for their money—and to attract new ones—Fishbrain wanted to make BiteTime as accurate as possible. “We have data on 7 million catches from our user base, of which we use 2.5 million as a basis for our training sets. We wanted to make better use of that data to provide a better service.” - Rickard Svedenmark, chief technology officer at Fishbrain. Most fishing forecasts use weather data coupled with assumptions about how likely a fish is to appear under given conditions. But Svedenmark figured Fishbrain could build machine-learning models based on its users’ catch data to predict more accurately the likelihood of catching fish. |