Legacy database technology was focused on analyzing historical data to gain an rear-view understanding of business performance. While it is important to analyze where the business is coming from, in order to gain the competitive advantage and differentiate your application it is critical to utilize deep learning and take action in-event; with the end goal of driving desirable business outcomes.
Building a robust predictive analytics model is only half the battle. Utilizing the model in production for real-time decision making is the key element of Machine Learning, this however is easier said than done. Most organizations have historical data stored in multiple places such as a data warehouse, data lake, ERP system, and more. In addition to existing data, a high volume of data is constantly streaming in from multiple sources at a very high velocity. In an production environment, the model needs to continually ingest, train on historical data and operationalize in real-time at very low latency.
VoltDB was designed for operationalizing Machine Learning in real-time. VoltDB can seamlessly:
VoltDB now offers a machine learning module, VoltML. Learn more in the release notes.
By changing your fraud system from a post-transaction detection system to a proper in-transaction prevention system, you can lower operating costs, reduce false positives, and stop fraud as it happens.
A real-time application or service must predictively and actively engage customers with highly personalized experiences. Achieving this with fast data requires tools that can collect, explore, analyze, and act on multiple streams of data instantaneously. These tools allow businesses to make data-driven decisions using insights from real- time analytics against fast moving data.
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