SUPPORTING AI/ML WORKLOADS ACROSS DIFFERENT INDUSTRIES:
Feature Processing Pipeline
Machine learning models require consistent and up-to-date features for both training and inference. Discrepancies between the data used during model training and the data available during inference can lead to issues such as data drift and degraded model performance.
Why Timeplus?
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Unified data processing
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Low-latency feature generation
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ASOF JOINs
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Mutable streams
The Outcome:
Timeplus ensures a consistent and temporally accurate feature store that bridges the gap between training and inference environments.
Models stay accurate and reliable, enabling faster, more informed decisions from live data.
Data Context for LLMs/Agents
Modern LLM applications and AI agents need fresh, context-rich data to deliver accurate responses and smart decisions. Relying on static or batch-processed data leaves them with outdated insights, leading to missed opportunities, poor recommendations, and subpar user experiences. Without real-time data, LLMs lack the context needed to understand current conditions and make timely decisions.
Why Timeplus?
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Live data streaming to LLMs
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Contextual data enrichment
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Dynamic knowledge updates
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Structured context generation
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Temporal context presernation
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Multi-modal data integration
The Outcome:
LLM applications and AI agents can deliver more accurate, context-aware responses by using real-time business data, user activity, and system state through Timeplus.
This leads to better customer experiences, more relevant recommendations, and intelligent automation that adapts to current conditions, not outdated information.
Anomaly Detection
Detecting anomalies in real-time is crucial for applications such as fraud detection, predictive maintenance, and security monitoring. Delays in identifying anomalies can result in missed opportunities for intervention and increased risk.
Why Timeplus?
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Real-time data processing
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SQL-based queries
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Alerting and visualization
The Outcome:
With Timeplus, teams can detect anomalies the moment they occur, minimizing response times and preventing escalated issues.
Whether it's flagging fraudulent transactions or identifying equipment failures, Timeplus enables continuous monitoring and fast action.
Trusted by industry leaders with mission-critical workloads:
Discover End-to-End Capabilities
Unified Streaming and Historical Processing
Timeplus combines streaming and historical data processing, simplifying the architecture of AI/ML pipelines and ensuring consistency across data sources.
Ultra Low-Latency Processing
With sub-second processing times, Timeplus ensures that machine learning models operate on the most current data, enhancing their responsiveness and accuracy.
SQL-Based Data Transformation
Timeplus's support for SQL allows data engineers to define complex data transformations and feature engineering tasks without the need for specialized programming skills.
User-Defined Functions (UDFs)
With JavaScript/Python UDFs, you can extend your processing workflows beyond SQL, facilitating advanced anomaly detection and feature engineering techniques.
Auto-Scaling Materialized Views
Timeplus supports materialized views, allowing for the pre-computation and storage of frequently accessed data, improving query performance and reducing computational overhead.
Alerting and Visualization Integration
Seamlessly integrate with alerting systems and visualization tools, such as Grafana, providing timely notifications and insights into data anomalies and system performance.
Resources from our team
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USE CASE
Real-Time AI/ML Pipeline and Alerting
Timeplus enables the seamless integration of real-time data streams into machine learning workflows, facilitating the construction of low-latency feature stores and the deployment of real-time anomaly detection systems. By consolidating both streaming and historical data processing into a unified platform, Timeplus simplifies the complexities associated with real-time AI/ML pipelines.
In the realm of AI/ML, processing and analyzing data in real-time is crucial. Traditional batch processing methods often introduce delays that can render insights obsolete by the time they are generated.
Timeplus addresses this challenge by providing a platform that supports the continuous ingestion, transformation, and analysis of data streams, ensuring that machine learning models operate on the most current information available.