W6P An Enterprise Platform for Getting AI from Concept to ProductionNEW!

February 13, 2019

2:15 pm - 5:30 pm

Duration: Half Day Course

Prerequisite: None

Learn how to build a state-of-the-art AI platform using open source software. Enterprise-grade AI platforms enable data scientists to train, optimize, and deploy models, data engineers to integrate batch and streaming data sources at scale, data analysts to explore and visualize data, and DataOps experts to operate and secure the system. Learn how real-world systems that apply machine learning, deep learning, NLP, data mining, and other data science techniques are built—and how you can enable your organization to take AI from concept to production.

You Will Learn How To

  • Define an AI platform, its key requirements, and the roles needed to build AI solutions
  • Deliver enterprise-grade data integration, wrangling, and quality with Nifi, Kylo, and Spark
  • Enable interactive, code-free, near real-time data exploration with the Elastic stack
  • Provide data scientists with productivity tools using JupyterLab and JupyterHub, enable training models on GPUs, and support high-compliance environments
  • Enable collaboration, versioning, and reproducible experiments
  • Publish predictive models to production as robust, scalable, secure, versioned APIs
  • Support a heterogeneous collection of machine learning and deep learning frameworks such as Tensorflow, PyTorch, Spark ML, Scikit-learn, R, and others on one platform
  • Monetize, meter, and secure model and data APIs published to third parties
  • Deploy, scale, secure, and monitor the platform using Kubernetes, Calico, and Prometheus
  • Deliver unified identity management and single sign-on using KeyCloak
  • Integrate these components into an existing enterprise architecture

Geared To

  • Software and enterprise architects
  • Engineering and data science leaders
  • CTOs, CIOs, VPs R&D, etc. looking to scale AI beyond proofs of concept

Register Online

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TDWI Las Vegas

Caesars Palace
Las Vegas, NV
February 10–15

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