You have built an Apache Spark data pipeline and trained machine learning models in hosted notebook environment, TensorFlow toolkit or scikit-learn scripts.
Now scale your process and operations for research and development of machine learning applications to support multiple data science teams, hundreds of training pipelines, thousands of machine learning models that serve predictions for real customers in real time.
Make your Spark operations serverless for data scientists, engineers, and multi-tenant applications. Hydrosphere.io increases the reliability of your Spark jobs, thereby saving the cluster resources and increasing the productivity of data scientists and engineers. Unlock new revenue streams by exposing Spark job server REST API for interactive applications to business users and tenants.
Deploy your machine learning zoo of sckit-learn, Spark ML, TensorFlow, fastText, xgboost models as end-to-end prediction pipelines. Power smart applications for your users with realtime serving REST API.
Hydrosphere.io greatly decreases engineering and operations burden and accelerates time to value for data science projects. and accelerates time to value for data science projects.
Deploy machine learning models as Elasticsearch, Spark SQL, Cassandra or Redshift User Defined Functions (UDFs), so web engineers can seamlessly integrate machine learning capabilities into existing applications.
Hydrosphere.io simplifies querying and scoring machine learning algorithms from the application stack.
Gain end-to-end quality of your data transformation, training, and prediction pipelines to identify the data quality issues, side effects and model degradation trends before they start affecting your business.
Hydrosphere.io provides anomaly detection and pattern recognition components designed for the monitoring data and machine learning heavy applications. It greatly impacts customer experience and reliability of your data driven business.
Hydrosphere.io is agnostic to your infrastructure, Apache Spark backend and machine learning frameworks.