Machine Learning Platforms for On‑Chain Data Analysis covers the end‑to‑end systems needed to turn raw blockchain and crypto market feeds into validated, production ML outputs and interactive visualizations. The topic spans data inputs (crypto market data APIs, on‑chain indexers, mempool and oracle feeds), cloud data platforms and database connectors for durable storage, pipeline orchestration and scheduling, data cataloging and lineage for reproducibility, model evaluation/monitoring, and dashboarding for analysts and traders. This area is timely because on‑chain data volumes and cross‑chain complexity continue to grow, regulatory and risk teams demand traceable model decisions, and teams increasingly deploy lightweight inference at the edge or via cloud integrations. Practical ML use cases include time‑series forecasting, anomaly and fraud detection, graph neural networks for wallet/contract relationships, NLP for labeling narratives, and causal or counterfactual analyses to support risk review. Model monitoring, explainability and dataset lineage are now first‑class concerns as models affect trading, risk and compliance decisions. Key tooling patterns: orchestration platforms (Dagster) manage pipeline scheduling and testing; MCP servers and database connectors (MCP Toolbox for Databases, Supabase MCP) standardize access to storage and enable LLMs or tooling to operate on live data; evaluation and tracing platforms (Arize Phoenix MCP) give unified APIs for experiments, datasets and traceability; and visualization servers (Vizro, AntV Chart MCPs) automate chart generation and dashboard templates. Together these components enable repeatable ETL, model training, evaluation, deployment, and explainable visualization. Selecting a stack requires balancing latency, scale, lineage needs and interactive visualization goals. Emphasizing standardized connectors (MCP), cataloging and monitoring reduces integration drag and improves auditability for production on‑chain ML workflows.