TuneSage

TL;DR;

TuneSage is a three-phase analytics-to-ML pipeline and streaming-era A&R copilot that helps indie artists and labels transform raw Spotify chart data and audio features into actionable insights, predicts first-month streaming traction for unreleased tracks, and exposes the model via a lightweight web app.

PROJECT CONCEPT

TuneSage: A Streaming-Era “A&R Copilot” for Indie Artists & LabelsA self-updating platform that ingests public Spotify chart data and track-level audio features, surfaces descriptive insights, and predicts the first-month streaming traction of unreleased songs—then exposes the model through a lightweight web app an artist manager could actually use.

PHASE 1 ▶ DATA ANALYST

Scope of Work Highlights:• Repository & Environment: Initialize a public GitHub repo (MIT license) with a clear folder structure (src/, data/, notebooks/, etc.), set up an isolated virtual environment, and lock dependencies with pip-tools for reproducibility.• Data Ingestion & Validation: Automate ingestion of raw CSVs and API dumps into data/external/, validate schemas, and clean anomalies into data/processed/.• Exploratory Data Analysis: Profile distributions, missingness, and outliers in Jupyter notebooks, produce initial visualizations, and document insights in reports/eda.md.• Governance & Quality: Enforce code style with pre-commit hooks (Black, isort, Flake8), implement CI checks, and maintain a Data Source Inventory GitHub issue for provenance.

PHASE 2 ▶ DATA SCIENTIST

Scope of Work Highlights:• Feature Engineering: Create domain-informed features (aggregations, encodings, interactions) in src/features/, with unit tests to ensure correctness.• Model Training & Experimentation: Train multiple algorithms (linear models, tree ensembles, neural nets) in src/models/, track hyperparameters and metrics, and compare performance with cross-validation.• Evaluation & Selection: Evaluate models using business-aligned KPIs (ROC/AUC, revenue uplift, conversion rates) and select the best candidate.• Model Card & Reporting: Produce a model_card.md summarizing purpose, data lineage, fairness checks, and projected impact; publish executive-ready insights in reports/models/.

PHASE 3 ▶ ML ENGINEER

Scope of Work Highlights:• Packaging & Deployment: Containerize the final model using Docker or serverless functions; define infrastructure as code with Terraform or CloudFormation.• API Integration: Expose prediction endpoints and integrate with a user-facing web app; secure with token-based authentication.• Monitoring & Drift Detection: Implement real-time logging, performance dashboards, and automated alerts for model drift or data anomalies.• CI/CD & Governance: Automate testing and deployment pipelines, enforce code review and approval gates, and maintain audit trails for reproducibility and compliance.

CONCLUSION

With this structured, end-to-end framework, TuneSage empowers indie stakeholders to make data-driven decisions, from initial exploration through production deployment—bridging the gap between A&R intuition and predictive analytics.