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Execution Plan

The reported project schedule ran from mid-January 2025 through the end of April 2025. Sequential work was used where one output depended on another; UI, backend, and documentation tasks were parallelized where practical.

Milestones

MilestoneStatusOutcome
Problem and scope definitionCompletedDefined a bounded decision-support product
ML pipeline architectureCompletedEstablished feature, training, and inference flow
Service implementationCompletedDelivered Auth, Data, Pattern, Profile, and Orchestration services
Frontend integrationCompletedConnected the user interface to backend workflows
Docker deploymentCompletedProduced repeatable local and EC2-oriented execution
Testing and documentationCompletedVerified behavior and captured system design
Kubernetes and expanded CI/CDPlannedReserved as a later scaling stage

Week-wise progression

WeeksFocusResult
1Planning and scopeProject direction finalized
2-4ML architecturePrediction approach established
3-6Frontend/UIInterface foundation prepared
5-8Core microservicesAuth, Data, and Pattern APIs completed
7-9OrchestrationIntegrated analysis flow formed
8-10ProfilePortfolio and risk context added
10-11IntegrationEnd-to-end paths verified
11-12DeploymentContainerized execution achieved
12-13DocumentationReport, screenshots, and cleanup completed

Gantt chart

ForesightX Gantt chart

PERT chart

ForesightX PERT chart

Risks and mitigation

The primary technical risk was contract drift between independently developed services. Incremental integration and service-level tests reduced the chance of discovering incompatible response formats at the end of the project.

The primary delivery risk was time pressure near submission. Parallel frontend, backend, and documentation work preserved time for final integration and validation.