As businesses continue to search for new ways to grow their competitive edge, demand has surged for innovative technologies and tools that streamline data analytics, Machine Learning (ML) and Artificial Intelligence (AI). In financial markets, AI and Machine Learning are on their way to becoming the most influential technology to shape the future of trading and risk management. While most leading firms are building out their AI strategies and in early phases of deployment, successfully implementing ML models at enterprise scale remains a challenge.
Beacon Platform’s cloud-native infrastructure and comprehensive development tools are designed to support and scale the most complex financial services applications, and provide everything developers need to rapidly build, test, deploy and share trading and risk applications, analytics and models.
DataRobot’s cutting-edge cloud AI platform and next-generation AI models are designed to accelerate delivery of AI to production for all users, all data types, and all environments.
Combining Beacon Platform and DataRobot equips firms with the tools to easily and effectively build, test, and deploy sophisticated AI and machine learning models and quickly gain a competitive edge in their markets, including:
- Advanced-scaling tools
- Rapid application development framework
- Accelerated end-user readiness
Whether you are developing new trading strategies, creating risk management frameworks, or optimizing investment portfolios, the ability to implement ML/AI and Data Science directly into existing applications and workflows enhances your competitive edge.
In this webinar, Claus Murmann, Beacon’s Head of Partnership Solutions Engineering is joined by Peter Simon, DataRobot’s Managing Director and Practice Lead: Global Financial Services Data Science. They will show you how Beacon’s leading trading and quant development platform capabilities can be extended with next generation AI and machine learning models.
What they will cover:
- Installing CME DataMine plugin from Beacon’s App Store and using the tools to extract historical market data into a time series
- Loading data into DataRobot to build, train, and test a market prediction model
- Deploying the model using DataRobot’s Portable Prediction Server running on a Beacon compute pool
- Connecting compute model predictions with new data via REST API and running some simple trading strategy backtesting
