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Upsolver for Machine Learning on Amazon Web Services

Upsolver empowers data scientists and data engineers to build reliable models and make accurate predictions by unifying historical, live and labeled data in a visual platform.

Real-time data made simple

Up to 100x better performance

Self-service at scale

Accurate machine learning entails lengthy model training as well as real-time decisioning. Existing solutions require organizations to orchestrate two separate, complex data architectures for historical and real-time data (typically based on Spark + NoSQL databases), and to ensure these systems are relying on similar data for online and offline decisioning, which creates engineering bottlenecks around data preparation and restricting innovation.

Upsolver provides a single, visual platform for preparing both historical and live data, enabling data science teams to focus on actual data science and automating repetitive data engineering tasks. Streams are ingested into Amazon S3 and stored ready for exploration and model training in Sagemaker, Athena or Spark, while Upsolver Materialized Views enable you to run these models on real-time data with just a few clicks.

Key Features

Visual feature engineering: data scientists can easily create behavioral features for all S3 data in a visual interface without writing Java or Scala.

Schemaless data management and preparation: schema and statistics are automatically generated and transformations are tested interactively against actual data.

Streamline data engineering work: batch and streaming pipelines are fully automated, managed and scaled - including compression, columnar storage, compaction, and replay.

Prepare streaming and historical data in a single platform: join multiple streams in-flight without additional NoSQL databases, perform data validations and add enrichments.

Auto-scaling including reprocessing from S3: data scientists can easily create behavioral features using readily-available historical data.

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