Drive your Enterprise AI Strategy with Dataiku 5.0

Dataiku is the leading end-to-end platform for data teams. Built on top of the latest open source data science solutions, Dataiku offers  a visual user interface and a white-box approach that fosters collaboration among all types of users (from coders to non-coders alike).

The latest release, Dataiku 5.0, introduces full containerization capabilities, accessible deep learning,  a richer AutoML interface, and comprehensive documentation to its long list of new and improved Enterprise AI product features.

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360 Degree View

Always know what is deployed and where. A unified interface allows deployment status, infrastructure health, and model performance to be monitored in detail and tracked at a glance. 

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One-Click Deployment 

Save time and manage your models and APIs code-free. Leveraging automation reduces dependencies on architecture and IT Teams, allowing data scientists to self-manage deployments and rollbacks. 

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Infrastructure Agnostic

All aspects of a deployment are easily configurable for full flexibility and scalability. Employ the same seamless workflow whether you're deploying on premise or to the cloud using Docker or Kubernetes.

 

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Truly End to End

Fully backed by and integrated with Dataiku. A truly end-to-end solution that extends existing capabilities in machine learning and model design/automation, with the familiar collaboration features as standard.

What's New

Now with full containerization capabilities; deep Learning for the citizen data scientist; and comprehensive documentation with group discussions, wikis, and personalized project home pages, Dataiku 5.0 will drive teams to build and deploy Enterprise AI solutions at scale.

Dataiku 5.0 release notes

 

Full Containerization with Docker and Kubernetes

  • In Dataiku 4.3, we introduced Docker/Kubernetes for Single Click Model Deployment making it possible for a standard user to create a one-stop shop to deploy models... without having to distract IT or DevOps from their respective jobs. 
  • In 5.0, we’re taking Docker and the power of “containerization” one step further: to in-memory processing of Python & R recipes, as well as in-memory model training and scoring. The result? More scaling and easier isolation for resource usage.
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Deep Learning with Keras & TensorFlow now available in Dataiku’s Visual AutoML Interface

  • Dataiku 5.0 welcomes Keras, the neural network library written in Python, to its ever-growing family of supported open-source libraries and technologies. Users can now define the architecture of their deep learning models directly from the Visual Machine Learning Interface. From there, Dataiku automatically handles data preprocessing, model training, deployment, versioning, rollback, and monitoring.

  • Dataiku 5.0 supports training models both on CPU and GPU, including multiple GPUs. Plus, thanks to the availability of code samples and Tensorboard, even the members of the team who aren’t familiar with deep learning architectures can now participate in building and monitoring these more complex models and AI applications.
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Comprehensive Documentation with Group Discussions, Wikis, and Personalized Project Home Pages 

  • Dataiku Discussions: share knowledge across the organization with Dataiku Discussions. In 5.0, anyone can create, subscribe, comment, interact with teammates or subject matter experts.
  • Wikis: Just like any wiki out there, users can now create spaces within Dataiku for collaborative creation and editing of documentation. Within these wikis, associate Dataiku items together, from certain datasets to dashboards, or simply list the resources within Dataiku that are a propos to specific teams. Finally, use these wikis to welcome newcomers on-board with accurate and relevant team and project documentation.
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About Dataiku

Dataiku is the data-driven backbone for some of the world’s top enterprises, serving as a central hub for analytics and machine learning model deployment/management. In 2017, Dataiku doubled in size and tripled its revenue, culminating in a September 2017 announcement of their $28M Series B funding round led by Battery Ventures along with FirstMark Capital, Alven Capital, and Serena Capital. 

 

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