Implementing the Explainable Monitoring framework
To implement the Explainable Monitoring framework, it is worth doing a recap of what has been discussed so far, in terms of implementing hypothetical use cases. Here is a recap of what we did for our use case implementation, including the problem and solution:
- Problem context: You work as a data scientist in a small team with three other data scientists for a cargo shipping company based in the port of Turku in Finland. 90% of the goods imported into Finland arrive via cargo shipping at various ports across the country. For cargo shipping, weather conditions and logistics can be challenging at times. Rainy conditions can distort operations and logistics at the ports, which can affect supply chain operations. Forecasting rainy conditions in advance allows us to optimize resources such as human resources, logistics, and transport resources for efficient supply chain operations at ports. Business-wise, forecasting rainy conditions...
