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TDWI San Diego Conference

W8A Data Warehousing in the Age of Analytics & Big Data: Hadoop and the Data Warehouse

August 21, 2019

9:00 am - 12:15 pm

Duration: Half Day Course

Prerequisite: None

In this tutorial you will learn to architect an end-to-end, enterprise-grade data science platform using free open source software. Such AI platforms are required to build, deploy, and operate machine learning, deep learning, NLP, and other AI-based systems and must adhere to the highest standards in terms of scalability, security, robustness, and compliance.

You Will Learn

  • What a data science platform is, and which user roles are required to build AI solutions
  • How to deliver enterprise-grade data integration, wrangling, and quality with Nifi and Spark
  • How to deliver interactive, code-free, batch and streaming data discovery and analysis with ElasticSearch, Kibana, Timelion, and Spark SQL
  • How to deliver experimentation, modeling tools, and cutting-edge algorithm libraries for data scientists with Jupyter and JupyterHub while maintaining data privacy and security
  • How to enable collaboration, versioning, and reproducibility of data science experiments
  • How to publish predictive models to production as robust, scalable, secure, versioned APIs
  • How to support a heterogonous collection of cutting-edge machine learning and deep learning frameworks such as Tensorflow, PyTorch, Spark ML, Scikit-learn, R, and others on one platform
  • How to monetize, ration, and secure model and data APIs published to third parties
  • How to deploy, scale, protect, and monitor an enterprise-scale platform using Kubernetes, Ansible, Calico, Prometheus, and Grafana
  • How to deliver unified identity management and single sign-on using KeyCloak
  • Best practices on integrating these components into an existing enterprise architecture

Geared To

  • Software and enterprise architects in the data science space
  • Engineering and data science leaders
  • CTO, VP of analytics, and/or VP of data science, looking to scale AI beyond the initial projects

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