Build Forecast AI Automation System
Budget: $3,000 – $5,000 USD
I’m providing a full starter bundle for our Forecast AI initiative: the PDF brief that walks through objectives and user journeys, a step-by-step setup checklist, and the Mac-ready starter assets. Begin by studying these files so you understand the current flow and where the major touchpoints sit.
Once you’re familiar with the material, I’d like you to take the project from concept to production-ready release. That means:
• Produce a detailed technical blueprint outlining data pipelines, model architecture, fail-safes, and performance targets.
• Configure the development environment on macOS, using the supplied starter assets, then build out the core automation engine that ingests data, runs the forecasting models, and delivers results without manual intervention.
• Implement integration hooks for future connections to internal databases, third-party APIs, and ad-hoc user inputs.
• Create a clean, modular codebase with unit tests, logging, and monitoring baked in.
• Package a deployment script plus a concise operations guide so the system can be spun up on additional machines with minimal effort.
• Supply user-level documentation and a short screencast demonstrating the full end-to-end flow.
I’ll be available for quick feedback loops, but I expect you to drive best-practice decisions around libraries, cloud services, and model optimization. The deliverable is a fully automated Forecast AI system that a non-technical stakeholder can launch, monitor, and trust.
Once you’re familiar with the material, I’d like you to take the project from concept to production-ready release. That means:
• Produce a detailed technical blueprint outlining data pipelines, model architecture, fail-safes, and performance targets.
• Configure the development environment on macOS, using the supplied starter assets, then build out the core automation engine that ingests data, runs the forecasting models, and delivers results without manual intervention.
• Implement integration hooks for future connections to internal databases, third-party APIs, and ad-hoc user inputs.
• Create a clean, modular codebase with unit tests, logging, and monitoring baked in.
• Package a deployment script plus a concise operations guide so the system can be spun up on additional machines with minimal effort.
• Supply user-level documentation and a short screencast demonstrating the full end-to-end flow.
I’ll be available for quick feedback loops, but I expect you to drive best-practice decisions around libraries, cloud services, and model optimization. The deliverable is a fully automated Forecast AI system that a non-technical stakeholder can launch, monitor, and trust.