AWS meetup slides August

1. August 1st 2018
#73PRESENTS

2. Sponsors

3. What’s On Tonight
6:00 pm
1. AWS Rekognition
“Automating Video Editing with Amazon Rekognition” - John Rotenstein
6:20 pm
2. CloudHealth
“Managing Cost through Reservation Management” - Richard Economides
6:40 pm
Break
Have some pizza & beer, on us!
7:20 pm
3. PagerDuty
“Harness the noise, Evolve with confidence” - David Ridge
7:40 pm Networking

4. Presentation 1
John Rotenstein
Technology Consultant
“Automated Video Editing with Amazon Rekognition”

5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
John Rotenstein (jorote@amazon.com)
Technical Trainer, Amazon Web Services
Automated Video Editing with
Amazon Rekognition

6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Three examples of Amazon Rekognition with…
Still Images Video Files Streaming
Video

7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
1. Detecting faces in still images

8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Loop: Capture an image & draw rectangles
while(True):
# Capture frame
frame = capture.read()...
# Resize image for faster rekognition
image = capture.resize(frame, 0.15, ...)
# Detect faces in image
faces = rekognition.detect_faces(Image={'Bytes':image}, ...)
# Draw rectangle around faces
for face in faces['FaceDetails']:
is_smiling = face['Smile']['Value']
draw_rectangle(image, face['BoundingBox'], is_smiling)

9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Attributes returned by DetectFaces()
{
"FaceDetails": [ {
"AgeRange": {"High"..."Low"...},
"Beard": {...},
"BoundingBox": {"Height".."Left".."Top".."Width"..},
"Eyeglasses": {...},
"EyesOpen": {...},
"Gender": {...},
"MouthOpen": {...},
"Mustache": {...},
"Smile": {...},
"Sunglasses": {...}
] }

10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
2. Using Amazon Rekognition on Video Files
Find a person,
make a video of
just them

11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Create a Collection & Load Faces
$ aws rekognition create-collection
--collection-id trainers
$ aws rekognition index-faces
--collection-id trainers
--image "S3Object={Bucket=mybucket,Name=john.jpg}"
--external-image-id John

12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Search for faces
$ aws rekognition start-face-search
--video "S3Object={Bucket=...,Name=trainers.mp4}”
--collection-id trainers
$ aws rekognition get-face-search --job-id...

13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
DetectFaces() returns Timestamps and Faces
{ "Persons": [
{
"Timestamp": 7360,
"FaceMatches": [
{
"Face": {
"ExternalImageId": "John",
"Confidence": 99.99750518798828 ...}
}
],
},
{
"Timestamp": 7560,
...

14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Clip Stitching with Amazon Elastic Transcoder
Timestamps:
[99800, 99840, 100000, 100040, ...]
Convert into individual scene start/end times:
[(99800, 101480), (127520, 131760), ...]
Convert into clips for Amazon Elastic Transcoder:
[
{'Key': 'trainers.mp4',
'TimeSpan': {'StartTime': '99.8', 'Duration': '1.68'}},
{'Key': 'trainers.mp4',
'TimeSpan': {StartTime': '127.52’, 'Duration': '4.24'}},
...
]

15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Result:

16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
3. Open a door with your face (Rekognition Streaming)
+ =

17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Architecture diagram
Live Camera
Amazon Kinesis
Video Streams
Amazon Rekognition
Video
Face collection
1. Camera-captured video
streams are processed by
Kinesis Video Streams
2. Rekognition Video
analyses the video and
searches faces on screen
against a collection of
millions of faces
IoT thing
lightbulb
AWS Lambda Amazon Kinesis
Streams

18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Thank You
John Rotenstein
jorote@amazon.com

19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Want to Know More?
Talk to an AWS Team Member

20. Presentation 2
Richard Economides
Technology Consultant
“Managing Cost through Reservation Management”

21. Cloud Service Management
Modeling and Managing Reservations
Richard Economides
reconomides@cloudhealthtech.com
www.linkedin.com/in/richard-economides-50764622/

22. 2 © 2018 CLOUDHEALTH
®
TECHNOLOGIES INC.
VisibilityOptimisationGovernance
Define tagging taxonomy &
reporting requirements
Initial Cost & usage reporting
Rightsizing information
gathering & discovery
Wasted cost analysis
(identification)
Define financial management
policies
Phase 1
Onboarding
Reporting on RI utilization
Trending and forecast analysis
Rightsizing baselining &
Analysis (EC2 & EBS)
Reserved instance
procurement
Configuration policies
(Tagging compliance)
Operational management
policies (identify and terminate
zombie infrastructure)
Phase 2
Optimisations
Financial reporting (chargeback
& Budget tracking)
Container cost analysis and
showback
Rightsizing business workflow
On-going RI analysis and
procurement
Data Centre migration
Notify on Budget and cost
variance
Introduce smart workload
scheduling
Phase 3
Self Governing Cloud
Defining a Cost Optimisation Strategy

23. 3 © 2018 CLOUDHEALTH
®
TECHNOLOGIES INC.
Customer Success Stories
CloudHealth Reservation Management
“We manage a fleet of RIs worth millions; one
CloudHealth modification helped save several
thousand dollars in a week. Per year, I’d say
we’re saving hundreds of thousands.”
I have a policy setup that sends an email every
morning showing me which RIs aren’t being
utilised properly. We only use Convertible
Reservations, and this email triggers me to log in to
AWS and make an exchange for a different instance
type.
— Charmaine Honeycutt,
Systems Administrator
“
“
“
“
— Brent Strong, Manager of
Cloud Engineering & Operations

24. 4 © 2018 CLOUDHEALTH
®
TECHNOLOGIES INC.
• On-demand hourly discount
• Three purchasing options: All Upfront, Partial Upfront, No Upfront
• 1 or 3 year terms
• Can be modified in
limited ways
Think Vouchers - Not Instances
What Are EC2 Reserved Instances?
Offer Type Commitment Discount Range
No Upfront 1 Year 27%–50%
Partial Upfront 1 Year 32%–53%
All Upfront 1 Year 34%–53%
No Upfront 3 Year 32%-55%
Partial Upfront 3 Year 38%-74%
All Upfront 3 Year 38%-75%

25. 5 © 2018 CLOUDHEALTH
®
TECHNOLOGIES INC.
When to Use Standard vs Convertible Reservations
Standard Reservations Convertible Reservations
• When you want the biggest possible
discount on reservations, but in exchange
are willing to accept less flexibility in
changes
• When you only want to make a one year
commitment
• When you are willing to pay slightly more to
have significantly more flexibility in changes
to Reservations
• When you are willing to make a 3 year
commitment

26. 6 © 2018 CLOUDHEALTH
®
TECHNOLOGIES INC.
• Understand Cost and Usage patterns, opportunities for
Reservations and on-going auditing
• Reservation Procurement
▪ Producing an Optimized Reservation quote
• Ongoing Operations and Automation
▪ Modifying Reservations
▪ Automated Governance (Underutilized Reservation &
Expiring Reservations)
Reservation Lifecycle through CloudHealth
Demonstration

27. 7 © 2018 CLOUDHEALTH
®
TECHNOLOGIES INC.
Learn More About Amazon Reserved Instances
Download our free eBook:
The Ultimate Guide to Amazon EC2 Reserved
Instances
http://bit.ly/maximizeRI

28. 8 © 2018 CLOUDHEALTH
®
TECHNOLOGIES INC.
Analysis is critical to an optimal purchase
Include an operational framework for the purchase and
ongoing management of Reservations
Reservations is only one aspect of Optimizations
Contact CloudHealth today for a Free Trial
https://go.cloudhealthtech.com/free-trial-
signup?ref=footer
Key Take Aways

29. Thank You
Richard Economides

30. Break & Networking
• Refresh your drink
• Grab some pizza
• Make new contacts
• Enter the prize draw!

31. Presentation 3
David Ridge
Solutions Consultant
“Harness the noise, Evolve with Confidence”

32. Harness the Noise, Evolve with Confidence
David Ridge
Solutions Consultant

34. Digital Evolution is On-going

36. PROPRIETARY & CONFIDENTIAL 5
Platform for Action
Harness Digital Data Make Sense of Data Respond & Engage Teams Analyze & Learn
Incident
Containers
Social
Network
Microservices
Security
Apps & Services
Servers
Database
Cloud
Incident
Incident
Incident
Incident

37. Harness the Noise
6
millions of events
Deduplication & Filtering
10-90% reduction
Suppression
50-90% reduction
Routing
50-70% reduction
Alert Correlation
25-90% reduction
Handful of actionable
incidents
Triage Resolve PreventMobilize
98% event noise
reduction via
multi-stage process

38. 7
Real-time
Intelligent
Alert Correlation
7
5% free

39. Real-time
Incident Correlation

42. Alert Correlation
for
Previous Similar Incidents

43. World’s Most Successful Companies Trust PagerDuty
FINANCIAL RETAIL MEDIA TECH HEALTHCARE TELCO INTERNET
12

44. https://www.pagerduty.com/connect/sydney/

45. Draw Prize
This weeks winner is :

46. Thanks For Coming
Join Us Next Month – September 5th 2018
Presentations from
&
>> Register @ http://www.meetup.com/AWS-Sydney/ <<