Data drives your AI/ML pipeline. It’s critical for training, testing and validating AI/ML algorithms. In fact, the more data you feed AI/ML algorithms, the better they perform, and the more accurate and significant the results. Yet most enterprises are challenged with accessing, preparing and analyzing their massive data.
SQream DB interfaces with common machine learning frameworks like Spark MLib, R, and TensorFlow, and can feed them with fast data after it has been “sliced and diced” with standard SQL preparation techniques.
Accurately distinguishing cyber threats from legitimate activity during peak usage times is a challenge, and false detection could lead to the disruption of legitimate customer usage. To achieve accurate detection, AI models must be trained with massive amounts of data from security devices. The more data they are fed, the more accurate their predictions will be.
A large telecom provider integrated SQream with SAS Viya to rapidly ingest and analyze massive data from multiple sources, which was then used to train their AI algorithms to detect and prevent cyber and DDoS (Denial of Service) attacks on their vast network. The solution enables the analysis of more data over longer periods of time, resulting in extremely high detection accuracy and minimal false alarms. As a result, the company was able to reduce operational costs, and shift resources from investigating false threats to better handling real ones.
Learn how SQream enables the analysis of significantly more data faster, so you can achieve new and critical insights from your AI/ML models and algorithms.