Conjecture
A framework for building scalable machine learning models in Hadoop using the Scalding DSL.
What is Conjecture?
Conjecture is a framework for building machine learning models in Hadoop using the Scalding DSL. It enables statistical models to be integrated as components in product settings, handling extremely large data volumes through Hadoop integration and established ETL processes.
Data engineers and machine learning practitioners working with massive datasets in Hadoop ecosystems who need to build and deploy classification, recommendation, ranking, filtering, or regression models at scale.
Developers choose Conjecture for its seamless integration with Hadoop and Scalding, enabling scalable training on large datasets through mapper/reducer aggregation, and its flexibility in handling diverse inputs while supporting multiple linear classifiers with configurable parameters.
Overview
Scalable Machine Learning in Scalding
Use Cases
Best For
- Training machine learning models on extremely large datasets using Hadoop infrastructure
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