#CurrencyPairPrediction Real-Time Deployment of Supervised Forex Models Deploying supervised learning models for real-time forex trading involves turning predictive algorithms into actionable systems that operate with low latency and high reliability. Key challenges include: Latency and Speed: Real-time systems must process data and generate predictions within milliseconds to capitalize on fleeting market opportunities. Data Streaming: Continuous ingestion and preprocessing of live forex data is required, demanding efficient data pipelines. Model Performance: Models must balance accuracy with speed, often favoring lightweight architectures to meet time constraints. Robustness and Adaptability: Real-time models must handle noisy data, sudden market shifts, and adapt quickly to changing conditions (e.g., through online learning or periodic retraining). Infrastructure: Requires reliable deployment environments (e.g., cloud, edge computing), monitoring tools, and risk management systems to ensure performance and prevent costly errors. Effective deployment blends machine learning with engineering to create fast, stable, and adaptive trading systems.