Next '18 Extended Phoenix News: [masked] update) The rough structure for the event is like this: Google Home GiveAway and other giveaways based on registration numbers! So if you need to get back home after work: Stay till 5. We recommend bringing: --- Food and beverage will be provided (vendors TBA). If you have any allergen/dietary considerations, please let us know ASAP so that we can best accommodate. --- If you would like to apply to be a speaker or provide a demo of how you use Google cloud, please email a brief proposal to: roy.tokeshi (at) citrix.com --- Shout out to phoenixNAP for providing us with an awesome venue, and for Google for providing support for this event <3
Agenda:
1. Matt Gartner is a Customer Engineer at Google based in Boulder, CO. He has an extensive background in cloud IaaS, PaaS, SaaS design and networking. He will provide his insight into some of the key announcements from Next '18. Matt will also do an application building/deployment demo.
2. Hands-On Labs : Baseline: Data, ML, AI
a. Introduction to SQL for BigQuery and Cloud SQL
b. BigQuery: Qwik Start - Web User Interface
c. BigQuery: Qwik Start - Command Line
d. Bigtable: Qwik Start - Command Line
e. Bigtable: Qwik Start - Hbase Shell
f. Cloud Natural Language API: Qwik Start
g. Google Cloud Speech API: Qwik Start
h. Dataproc: Qwik Start - Console
i. Dataproc: Qwik Start - Command Line
j. Dataprep: Qwik Start
k. Google Cloud Datalab: Qwik Start
l. Cloud ML Engine: Qwik Start
m. Data Studio: Qwik Start
n. Google Genomics: Qwik Start
o. Dataflow: Qwik Start - Templates
p. Dataflow: Qwik Start - Python
q. Cloud Filestore: Qwik Start
r. Machine Learning with Spark on Google Cloud Dataproc
Processing Time Windowed Data with Apache Beam and Cloud Dataflow (Java)
s. Machine Learning with TensorFlow
t. Distributed Machine Learning with Google Cloud ML
u. Real Time Machine Learning with Google Cloud ML
v. Bayes Classification with Cloud Datalab, Spark and Pig on Google Cloud Dataproc
w. Machine Learning with Spark on Google Cloud Dataproc
x. Processing Time Windowed Data with Apache Beam and Cloud Dataflow (Java)
y. Machine Learning with TensorFlow
z. Distributed Machine Learning with Google Cloud ML
aa. Real Time Machine Learning with Google Cloud ML
bb. Bayes Classification with Cloud Datalab, Spark and Pig on Google Cloud Dataproc
cc. Citrix on GCP
dd. Stackdriver: Qwik Start
ee. Getting Started with Cloud KMS
4. VPC Deep Dive and Best Practices
5. Hybrid Cloud Notes from the Field: Connecting to Azure, AWS and On-premise
6. Certification: Design Methodology.
7. Lab Techniques for learning.
1. Participate in different event schedules.
a. Full event (9 hours)
b. Day event (4 hours)
c. Evening Event (4 hours)
2. Pick your track:
a. Technical: Sessions and discussion
b. Hands-on Labs
c. Combination of both!
If you can't get away after work: come after!
If you can fit the whole event in your schedule: cool!
Whatever hands-on level you want: 0 to 9 hours is available.
- a laptop or device with a dedicated keyboard
- power cable/battery bank
- headphones, if you'd prefer to do labs solo
