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With 1 week left to produce your annual operating plan, you find yourself tasked with budgeting for the entire year. You are responsible for predicting the expenses for all of your subsidiaries across five different cost centers in three countries. You’re looking at hundreds of predictions you need to come up with to report to the CFO. You look to your left and then to your right, and see your co-workers who have the same task.
Together, you all have thousands of predictions to come up with for the year. Luckily, you have 10 disparate data systems and 20 Excel files called plan_spend_final_v2_final_final_1.xlsx on the screen in front of you!
Clearly, this is a nightmare scenario, but chances are you experienced it wide awake the last time it occurred.
In this blog, we'll explore how MLOps can transform such challenges into streamlined, efficient processes.
MLOps, or Machine Learning Operations, is a framework that combines machine learning, data engineering, and DevOps practices to operationalize and scale machine learning models effectively. It encompasses the tools, processes, and workflows needed to develop, deploy, monitor, and maintain machine learning systems in production environments.
By leveraging MLOps, finance teams can build the foundation for predictive financial statements, enabling more accurate forecasting and better strategic decision-making.
Predicted financial statements (PFS) are forward-looking projections of an organization's financial performance, forecasting values for key accounts such as revenues, expenses, and profits. These forecasts are built using historical data, assumptions, and models to estimate future financial outcomes. Predicted financial statements are critical for financial planning and analysis (FP&A) teams, as they enable accurate budgeting, help set realistic financial targets, and support scenario planning. By forecasting various potential outcomes—such as changes in revenue or cost structures—FP&A teams can make data-driven decisions, manage risks, and align strategies to ensure the company’s success and deliver value to shareholders.
Scaling MLOps is essential for addressing the challenges of modern financial forecasting. MLOps serves as the foundation for managing the complexity and volume of financial data, enabling organizations to build reliable models and ensure seamless execution. By operationalizing machine learning, MLOps facilitates precision, efficiency, and scalability in predictive financial statements.
Time-series data, like revenue and expenses, often presents erratic fluctuations, anomalies, and missing values. These issues make financial forecasting challenging, especially when combined with exogenous variables such as market shifts. A robust MLOps framework provides tools to clean, align, and integrate complex data efficiently, allowing for more accurate and actionable predictions.
Accurate and interpretable models are essential for informed decision-making. MLOps frameworks ensure that predictions are not only precise but also transparent, fostering trust among stakeholders. By emphasizing clarity in how models reach their conclusions, organizations can maintain confidence and support strategic goals effectively.
As businesses grow, the need to scale prediction models becomes increasingly important. For large organizations with multiple subsidiaries, departments, or geographical locations, the number of variables and scenarios that need to be modeled can increase exponentially. Scaling prediction models to handle this complexity, while maintaining performance and accuracy, is a significant challenge. It’s essential to develop frameworks that can accommodate a wide range of scenarios without compromising the reliability of the forecasts.
While automation is essential for efficiently generating financial predictions, it’s equally important to maintain oversight and the ability to intervene when needed. Fully automated systems can quickly process large volumes of data, but they may overlook nuances or fail to account for unforeseen changes. Maintaining the option for expert judgment—whether it’s adjusting assumptions or correcting model outputs—ensures that the forecasts remain grounded in reality and are aligned with the company’s strategic goals. Balancing automation with expert oversight is key to ensuring the success and accuracy of financial predictions.
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Precanto’s advanced MLOps framework simplifies and scales financial model development. Features like Continuous Integration/Continuous Deployment (CI/CD), automated logging, and robust performance tracking enable organizations to deploy reliable predictive models quickly and securely. This infrastructure balances flexibility, scalability, and data integrity, meeting the diverse needs of businesses efficiently. For predicted financial statements (PFS), we leverage a modular and containerized framework that supports CI/CD, ensuring that model updates and improvements are deployed seamlessly.
Automated logging and metric tracking, implemented across all model types, form the backbone of our MLOps strategy. This system ensures consistent performance monitoring and enables the effective management of large-scale sub-model architectures like those required for PFS. By building this infrastructure, we have already achieved the flexibility needed to handle thousands of models efficiently, enabling rapid growth and adaptation to specific requirements.
Privacy remains a core principle of our approach, with strict data isolation to maintain security and trust. This combination of scalability, automation, and data integrity underscores the strength of our MLOps framework and its critical role in delivering reliable predicted financial statements.
Learn more about how Precanto keeps your data secure.
Precanto's MLOps framework is designed to handle diverse model requirements across various use cases by providing a modular architecture that supports seamless integration of any model type. For predicted financial statements (PFS), we specifically employ time-series forecasting models, leveraging their strengths in capturing temporal patterns and trends in financial data.
Model selection is a critical part of our process, achieved through comprehensive hyperparameter tuning and a rigorous comparison of error metrics. By evaluating each model's performance on key metrics, we ensure that the best-suited model is chosen for the task at hand, maximizing both accuracy and reliability.
Our approach also includes treating anomalies in the data, which often manifest as noise in time-series inputs. Addressing these irregularities ensures cleaner data and more robust predictions. Combined with our containerized and orchestrated training pipeline, this framework adapts efficiently while maintaining the flexibility to meet unique requirements for each forecasting scenario.
Precanto’s modular MLOps framework adapts seamlessly to diverse industries, regional standards, and unique business drivers. By optimizing model training through parallelization and efficient I/O management, Precanto enables businesses to handle complex financial forecasts at scale while maintaining accuracy. This tailored approach ensures actionable insights that evolve with organizational needs.
Revisiting that annual budgeting scenario: this time, instead of wrestling with endless Excel files and disparate data systems, you’re using a streamlined platform that delivers accurate, real-time predictions. With just a few clicks, you’ve integrated data from every cost center, identified anomalies automatically, and finalized forecasts across subsidiaries in three countries. No more chasing down numbers or deciphering cryptic file names—just confident, data-driven forecasting.
It’s not just about making workflows easier—it’s about empowering teams to unlock the true potential of predictive financial statements through MLOps to drive meaningful impact.
Learn more about how Precanto’s advanced MLOps framework can transform your Financial processes.
Schedule a demo to learn how Precanto can help your organization.
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