SQream Platform
GPU Powered Data & Analytics Acceleration
Enterprise (Private Deployment) SQL on GPU for Large & Complex Queries
Public Cloud (GCP, AWS) GPU Powered Data Lakehouse
No Code Data Solution for Small & Medium Business
Scale your ML and AI with Production-Sized Models
Elevate your machine learning models by training on production-sized datasets without scaling limitations, and deliver faster, more accurate insights.
SQream Blue is a cloud-native fully-managed data lake-house built for fast, reliable, and cost-effective data processing utilizing a patented GPU-acceleration engine. The platform enables easy data preparation and transformation from and to the data lake, for faster analytics and AI/ML
Eliminate traditional data limitations and train on vast and complete datasets for more accurate, reliable models. Leverage the full potential of GPU acceleration to boost both training speed and model complexity.
Reduce data preparation and model training times and bring your AI/ML solutions to market faster. Stay ahead of the competition by focusing on innovation instead of infrastructure management.
Remove the technical barriers that slow down your model development process. Let SQream handle the heavy lifting of data processing, enabling your data science teams focus on refining models for business growth.
Integrating RAPIDS with SQream, allows all data to stay within SQream where you can then run the ML model, eliminating the need to ingest, prepare, transform, and transfer the data to GPU memory to run RAPIDS. This simplified process saves significant time and resources.
Our advanced data processing capabilities streamline feature engineering so you can easily prepare even the most complex data structures for machine learning tasks. SQream + NVIDIA RAPIDS’ cuML integration lets you execute feature extraction and transformation tasks in parallel, reducing complexities and time to deployment.
SQream features native integration with RAPIDS cuML and other NVIDIA-supported open-source projects like Dask and cuDF. Take advantage of RAPIDS' GPU-accelerated ML libraries to simplify data processing and modeling workflows while harnessing the combined power of GPUs for superior performance.
Train your models on terabyte to petabyte-scale datasets without the need for down-sampling or compromising on model accuracy. Effortlessly scale your ML workloads and handle data sizes that were previously out of reach.
Reduce data prep time and streamline your ML workflows. Drastically cut data ingestion, transformation, and preparation time. Train your AI/ML models with lightning speed. Accelerate linear regression and XGBoost processes using GPU-optimized data.
Telecommunications companies can accelerate the development of predictive models for personalized recommendations, proactive issue resolution, and churn prevention. With GPU acceleration from SQream and RAPIDS, Telcos can significantly improve customer satisfaction and loyalty.
Read the case study
Detect fraudulent transactions faster and more accurately by training ML models on production-sized datasets without reducing dataset fidelity. This enhances the ability to spot anomalous patterns across millions of transactions.
Use machine learning to analyze customer behavior at scale, allowing you to make data-driven decisions in near real time. With GPU acceleration from SQream and RAPIDS, even the largest transactional datasets can be processed quickly to optimize marketing campaigns and improve customer retention.
Train predictive maintenance models on terabytes of sensor data to predict equipment failures before they occur. With the combined power of SQream and NVIDIA RAPIDS, data ingestion and feature engineering are streamlined, enabling real-time insights from complex datasets.
