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Wize Analytics for Tech Experts

Quick experimentation and operationalization for machine learning at scale.

Storage and Compute Agnostic

Wize Analytics can run on-premise or in the cloud — with supported instances on Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure — integrating with storage and various computational layers for each cloud.

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Any IDE, Git-Enabled

Wize Analytics provides an integrated development environment for Python, R, Julia, and Scala from which you can transparently access data sources without having to manage connectivity issues. Leverage Wize Analytics:

  • In a “Notebook” style (with Jupyter Notebook).
  • In a “Visual Flow” style (by creating a flow of computation represented graphically in the tool).
  • By connecting your own IDE (SublimeText, Visual Studio) to the platform. 

All developments can be managed in Git.

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Spark & Kubernetes Clusters: Fully Managed (at Scale)

Wize Analytics can either leverage existing Spark and Kubernetes clusters or create and manage its own clusters (leveraging cloud platforms). 

Powerful Extensions via Wize Analytics Plugins

Wize Analytics Plugins enable developers to take control and expand any part of the platform by building powerful extensions to out-of-the-box functionality using Python or Java. Wize Analytics plugins can help connect to new data sources, provide and encapsulate a new algorithm visually for non-coders, integrate an IT process within Wize Analytics, and much more. Wize Analytics can be further extended via APIs, and it integrates with Jira and Jenkins.

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Build a Robust Data Architecture, End-to-End

Wize Analytics architecture is built around a pattern that systematizes the push down of computation into existing technologies, and it provides all the building blocks to enable data architects to build their own robust data architecture:

  • Data validators to protect the architecture against changes in underlying data sources.
  • Robust deployment with auto-scale, versioning, and rollback for both batch data pipelines and real-time model scoring. 
  • A smart data reconstruction engine for efficient incremental data recomputation.

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Create Thousands of Models to Find the Best One

Leverage Wize Analytics AutoML to quickly create best-in-class models with automated testing of multiple algorithms and parameters. Or take full control over all training settings, algorithm settings, and the optimization process, including writing your own custom models and using advanced deep learning models

Wize Analytics supports the most popular machine learning engines — Python, Spark, H2O, TensorFlow — and has many different core algorithms.

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Automate and Monitor With APIs

Wize Analytics provides an extensive API for platform setup, administration, and deployment (including automating the deployment of the full solution or new services). Administration extensions let you integrate Wize Analytics within your existing monitoring IT stack.

EXPLORE WIZE ANALYTICS DATAOPS CAPABILITIES

Get Started with Wize Analytics