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Course Overview and Problem Framing

Welcome, Outcomes, and Success Criteria

Problem Definition and Business Requirements

Data Strategy, Ingestion, and Feature Engineering

Data Sources and Modeling the Cash Flow Domain

Data Ingestion, Quality, and Governance

Feature Engineering for Cash Flow Forecasting

Forecasting Methods and Model Development

Method Selection and Baselines

Modeling Techniques and Evaluation

Uncertainty, Scenario Planning, and Ensembles

Architecture, Implementation, and MLOps

System Architecture and Data Pipelines

Training, Serving, and Deployment

Monitoring, Alerting, and Model Maintenance

Application Experience and Interfaces

Designing the User Experience for Finance Teams

APIs, Integrations, and Security

Hands-on Project and Assessments

Capstone Project: Build and Deploy a Cash Flow Prediction Application

Quiz: Problem Framing and Data Strategy

Quiz: Feature Engineering and Quality Controls

Quiz: Forecasting Methods and Evaluation

Quiz: Architecture and MLOps

Quiz: Application UX, APIs, and Security

Final Course Survey

Master the full lifecycle of building a production-grade cash flow prediction system—from business problem framing and data strategy to modeling, deployment, and continuous monitoring. End-to-End Cash Flow Prediction: Design to Deployment is a practical, project-based course designed for data scientists, ML engineers, analytics engineers, and finance technologists who want to deliver reliable short-, mid-, and long-horizon forecasts that finance teams trust. You’ll learn how to define scope and KPIs with stakeholders, model the cash domain (AR/AP, treasury, payroll), build robust ingestion and quality checks, engineer leak-free features, and select the right forecasting methods for your horizon and data volume. Go beyond notebooks: implement an MLOps-ready architecture, automate training and serving, instrument monitoring and drift detection, and design a review workflow with explainability for CFOs, treasurers, and FP&A analysts. By the end, you will deploy a working application with documented baselines, backtesting, prediction intervals, and a lightweight UI for forecast review and scenario planning. This course emphasizes auditability, security, and operational excellence so your model can stand up to real-world SLAs and compliance requirements.

End-to-End Cash Flow Prediction: Design to Deployment

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            <p>Cash flow is the heartbeat of any business, and accurate, explainable prediction is the difference between confident decisions and costly surprises. End-to-End Cash Flow Prediction: Design to Deployment guides you through building a production-grade forecasting application that finance teams can trust and engineering teams can operate. Anchored in real business requirements, the course covers the full lifecycle—from problem framing and KPI definition to model development, system architecture, and ongoing MLOps. You will translate stakeholder needs into measurable targets (MAPE, WAPE, RMSE, bias), set SLAs and acceptance thresholds, and account for auditability, explainability, and regulatory constraints. By the end, you will understand how to scope operating, investing, and financing cash flows at the right granularity and horizon for decisions around liquidity, runway, and working capital.</p>

<p>You will build a robust data foundation by modeling the cash flow domain across ERP/GL, bank statements, AR/AP, payroll, and treasury systems, distinguishing events from balances and designing entities, facts, calendars, and currency handling. The course then moves into ingestion patterns (batch, streaming, CDC), data quality and reconciliation, and governance (lineage, catalog, PII, access controls), along with storage choices and performance trade-offs. With that in place, you will engineer leak-free, high-signal features: calendar effects, AR/AP behaviors, seasonality and trends, exogenous macro and FX signals, and event flags—while using rolling windows and lagged relationships appropriately for time-series rigor.</p>

<p>On modeling, you will evaluate statistical baselines and advanced methods—ARIMA/ETS, Prophet, TBATS, state-space models; machine learning with gradient boosting and regularized regression; and sequence architectures such as LSTM and Temporal Fusion Transformer. You will practice segmentation, cold-start strategies, hierarchy reconciliation, and disciplined backtesting with rolling-origin evaluation. Uncertainty is first-class: quantify risk with prediction intervals, quantile regression, Monte Carlo scenarios, and stress testing for DSO/DPO, FX swings, sales shocks, and seasonality shifts. Interpretability techniques (e.g., SHAP) will help translate model drivers into finance-ready insights.</p>

<p>From there, the focus shifts to architecture and operations. You will assemble a reference system with ingestion, feature store, model service, cache, and APIs; compare batch versus real-time prediction flows; and orchestrate workflows using tools like Airflow or Dagster. The deployment track covers experiment tracking and registries, model packaging and serving patterns (batch jobs, REST/gRPC, serverless), and CI/CD spanning code, data, and models—wrapped with security fundamentals (secrets, IAM, network policies, audit trails). Monitoring closes the loop: pipeline health, latency, and cost dashboards; drift and forecast error tracking by segment; human-in-the-loop overrides; and safe retraining, A/B or shadow deployments, and rollbacks.</p>

<p>Because adoption requires great interfaces, you will design an experience tailored to CFOs, treasurers, FP&amp;A analysts, and accountants. The application will support forecast review, overrides, scenario creation, and approvals, with clear visualizations of inflows/outflows, liquidity runway, and variance to plan, plus embedded explainability and confidence intervals. You will also define APIs and integrations for banking, ERP, and data warehouse systems, with attention to RBAC/ABAC, auditing, masking, caching, pagination, and rate limits that ensure performance and safety in enterprise environments.</p>

<p>Learning culminates in a capstone where you build and deploy a working cash flow prediction application. You will define scope, KPIs, and acceptance criteria; stand up ingestion and data quality checks using provided or synthetic datasets; engineer features; establish and beat baselines; and run disciplined backtests. Finally, you will deploy a serving endpoint, craft a lightweight review UI for finance users, implement monitoring and alerts, and document a retraining plan—then present your results in a final report and demo. Quizzes throughout reinforce problem framing, data strategy, feature engineering, modeling and evaluation, architecture and MLOps, and application UX, APIs, and security, ensuring you graduate with both conceptual mastery and hands-on competence.</p>
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