Challenges
ML has emerged as a transformative force in financial systems, driving innovations in areas such as algorithmic trading, risk management, and smart order routing. However, while the potential of ML is vast, its deployment presents several challenges. From the nuances of training models on historical data to the intricacies of real-time prediction and production deployment, financial professionals need to navigate a complex landscape. This section will discover some of the key challenges faced when integrating ML into financial systems.
Differences between training models with historical data (offline) and making predictions in real-time
Training ML models on historical data offers the advantage of a controlled environment. Engineers and data scientists can validate models, refine hyperparameters, and evaluate performance metrics using vast amounts of past data. However, transitioning from this offline training to real-time predictions introduces several challenges...
