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Cloudata Inc - Offering Multiple Boot Camps in 250+ Cities closest to you!

Universal/Global Deep Learning of PB-Scale AI Big Data Cloud w/t TB-HandsOn Boot Camp - Covering All Major High-tech Hubs Worldwide, 23 Meetup Chapters and 3 different Boot Camps scheduled at 250+ cities

- https://www.meetup.com/pro/Global-Deep-Learning-of-PB-Scale-AI-Big-Data-Cloud-IoT-Bot-BlockChain-Boot-Camp/

- https://www.hwswworld.com/index.php?route=product/category&path=139 (seeking Universal & Global Partners)
Boot Camp #1 - PB-Scale 40-Node 2TB Deep Learning AI Big Data Cloud Boot Camp
Boot Camp #2 - PB-Scale 40-Node 2TB Immutable Data Lake Boot Camp
Boot Camp #3 - PB-Scale 40-Node 2TB Real-Time Data Pipeline Boot Camp
Boot Camp #4 - PB-Scale Multi-Node AI Deep Learning Big Data Container Cluster Boot Camp
Boot Camp #5 - Build & Operate Multi-Million-Dialogue ChatBot in Big Data AI Cloud Boot Camp

We fly to your city once you make an order even it is not part of our global 250+ city list
- https://www.eventbrite.com/o/cloudata-inc-13378491451 (Covering 250+ Cities Globally)

Follow our Real-time AI Big Data Cloud Thinktank at @ClouDatAI - Global Top 100 Most Prolific Twitter
@ClouDatAI
train@hwswworld.com

You must know DevOps, the merge of Software Developer and System Administrators in Cloud Era, but do you know the further merge of Data Scientist and Devops in AI Big Data Cloud Era - we call it DataDevOps?

It does not matter whether you are at senior-level or entry-level in Software Development/System Administration/Data Science/AI, as our advanced boot camps are brand new, with so called leap-frog effect, you are lucky to leap forward bypassing legacy technologies. The goal of the boot camp is to build & operate data pipeline and/or data lake for big data-enabled AI in cloud, and we will provide sample code in Python for Hadoop & TensorFlow and/or Scala for Spark (if you are new to them) for you to proceed and enhance and expand, your mileage may vary depending on your background and experiece, every attendee will benefit significantly

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