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Schema Definition

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Linked challenge: Predictive Mining Capital Allocation

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Linked challenge

Predictive Mining Capital Allocation

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Create a Weaviate schema in Python for a class named 'MiningProject'. Include properties for 'projectName', 'commodityType', 'capexUSD', and 'geopoliticalRiskLevel'. Enable the 'text2vec-transformers' module for the description property.

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implementation

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Linked challenge

Predictive Mining Capital Allocation

With battery prices hitting record lows and commodity prices like gold and oil fluctuating, mining companies must decide which capital-intensive bulk projects to prioritize. This challenge tasks you with building a decision-support system that evaluates mining project viability. You will use the Mistral Nemo model for advanced multilingual analysis and TorchServe for deploying a custom price-prediction model. The core of the application will be a vector database that stores complex project profiles, including capital expenditure (Capex), geographical risks, and mineral types. You will implement a RAG pipeline that allows users to query which 'Megamines' are most resilient to a low-battery-price environment. Additionally, you will explore the use of the NNI (Neural Network Intelligence) toolkit to optimize the hyperparameters of a regression model predicting mineral price trends for 2026.

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