ARTIFICIAL INTELLIGENCE • MACHINE LEARNING
Real-Time Feature Platform
For a financial institution, systems process numerous transactions every second, each encompassing data points such as transaction amount, location, and time. These systems may generate millions of data points per second or more. Real-time machine learning must utilize this data to process features and train a fraud detection model as data arrives, enabling immediate insights and actions. Timeplus offers low-latency, real-time feature generation that maintains a consistent training and serving data pipeline, thereby effectively reducing the total cost of ownership (TCO) of the fraud detection platform.
ARCHITECTURE
WHY TIMEPLUS?
Consistent Features
Ensure consistency between online and offline features, offering a unified platform for both ML training and real-time inference.
This consistency eliminates discrepancies between training data and production data, leading to more accurate and reliable models. By maintaining the same feature set across environments, Timeplus helps streamline the ML lifecycle, reducing the risk of model drift and ensuring robust performance in real-world scenarios.
Low-Latency Feature Generation
Generate fresh features with ultra-low latency, crucial for real-time ML applications.
Timeplus excels in Whether it's processing live data streams or transforming raw inputs into actionable features, Timeplus ensures that your models are always working with the most up-to-date information. This capability is vital for dynamic environments where real-time decisions based on the latest data can significantly impact outcomes.
Simplified Infrastructure
Simplify the complex infrastructure typically required to manage both online and offline data for ML.
By providing a single platform that handles data ingestion, transformation, and feature generation, Timeplus reduces the need for multiple tools and integrations. This streamlined approach not only saves time and resources but also minimizes potential points of failure, making your ML pipelines more efficient and reliable.
Discover End-to-End Capabilities
ASOF Join
Maintain point-in-time correctness in your ML models, allowing you to accurately associate events and features with the right temporal context during both training and inference.
Mutable Streams
Leverage mutable streams in Timeplus to correct and update labels in real-time as new information becomes available. Enable your models to remain accurate and up-to-date with the latest data.
Tumble/Hop Window Aggregation
Timeplus provides Tumble and Hop window aggregation capabilities to generate features in both real-time and from historical data. This allows you to create and maintain a comprehensive feature set that captures.
Partition By
Perform detailed sub-stream analysis by partitioning data using specific keys or attributes. This enables fine-grained feature generation and model evaluation within particular segments of your data.
Python SDK
The Timeplus Python SDK allows seamless integration with popular Python-based ML tools and libraries. Easily incorporate Timeplus into existing workflows.
Remote UDFs
With remote User-Defined Functions (UDFs), integrate ML models directly into SQL queries to enable real-time model serving. Apply trained models to live data streams and generate predictions.
Monitoring
Monitor your models' performance in real-time with dedicated dashboards, using built-in dashboards in Timeplus, or integrate with tools like Grafana.
Alerts
With alerts, trigger automated actions or notifications, ensuring that suspicious activities are identified and addressed immediately, minimizing risk and preventing further damage.
Resources
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