Why Hydrosphere.io?

For manager

Get visible ML implementation results fast and in predictable fashion

  • 1.

    Reduce managerial uncertainties in ML ops environment
    Once adopted hydrosphere.io makes ML DevOps operations (like model deploy, retraining and redeploy) smooth and reproducible any moment, consuming little and predictable time.

  • 2.

    Bring cross-operational efficiency into your data science team
    Delivering transparency to operational environment hydrosphere.io facilitates end-to-end responsivity among data, engineering, QA and others ML-ops engaged teams for delivering high quality end-user products.

  • 3.

    Get the competitive edge
    Drive adoption of analytics services across the enterprise and turn it into competitive advantage of your business.

For Data Scientist

Simplify your day-to-day operations

  • 1.

    Deliver value to the end users
    10 years ago, the main deliverable for scientists was an academic paper. 5 years ago, data scientists learned R & Python. Now hydrosphere.io allows data scientist to expose analytics to the end customers and own the whole product life cycle.

  • 2.

    Move faster
    The right level of abstraction from a big data infrastructure components allows data scientists to deploy new models multiple times a day no waiting for DevOps and engineering teams.

  • 3.

    Improve the quality of your data pipelines and machine learning models.
    Tap into production deployment and predictive monitoring process in a simplified data scientist friendly workspace.

For Big Data Engineer

Automate model serving, delivery, and monitoring of data pipelines

  • 1.

    Enable real-time model serving
    Traditional big data processing happens offline in a batch jobs. Model serving for online applications is traditionally a missed component in a big data ecosystem. Hydrosphere.io allows you to expose your existing Apache Spark models for serving online workloads.

  • 2.

    Move to reactive architecture
    Streaming use cases are essential for building event-driven services and reactive UI applications. Hydrosphere.io middleware allows web developers to subscribe, parameterize and interact with Apache Spark Streaming jobs and receive results streams using any of messaging engines you use.

  • 3.

    Improve quality and reliability of your data pipelines.
    It is challenging to perform unit tests upon big data pipelines and models because of their stateful nature. Hydrosphere.io simplifies production testing by providing intelligent monitoring purposely built for timely detecting and preventing data quality and model quality issues.

  • 4.

    Introduce analytics microservices
    Follow the modern architecture patterns by exposing fine-grained analytics services to be used by other microservices and applications across the enterprise.

For Web Developer

Analytics as a Service

  • 1.

    Make your applications smarter
    Routine CRUD applications should be deprecated. Add predictive and interactive features into your product to improve the engagement with your users.

  • 2.

    Tap into big data analytics as a service
    No more shared databases and low-level APIs. Hydrosphere.io allows you to work with predictive services on a right degree of abstraction and focus on UI/UX.