AWS has been supporting companies across Australia and New Zealand to put their most innovative tools and technologies to work to achieve their business needs and goals. AWS and our ecosystem of partners has helped the likes of CP Mining, IntelliHQ, WesCEF, Oz Minerals, Woodside and many more to modernise their analytics and data architecture in order to successfully generate business value from their data.
This event series aimed to educate customers with a broader understanding of how to build next-gen data lakes and analytics platforms and make connections with AWS.
32. Our mission at AWS
Put machine learning in the
hands of every developer
33. 200 new features and services
launched this last year alone
Unmatched flexibility
Broadest and
deepest set of AI
and ML services
70% cost reduction
in data-labeling
10x faster performance
75% lower inference cost
Accelerate your
adoption of ML
with SageMaker
Built on the most
comprehensive cloud
platform
AWS Named as a Leader in
Gartner’s Infrastructure as a Service
(IaaS) Magic Quadrant for the 9th
Consecutive Year
W H Y A W S F O R M L ?
34. AI Services
Broadest and deepest set of capabilities
T H E A W S M L S T A C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Services
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
& C O M P R E H E N D
M E D I C A L
L E X F O R E C A S TR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T P E R S O N A L I Z E
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
FRAMEWORKS INTERFACES INFRASTRUCTURE
ML Frameworks + Infrastructure
F P G A SE C 2 P 3
& P 3 D N
E C 2 G 4
E C 2 C 5
I N F E R E N T I AG R E E N G R A S S E L A S T I C
I N F E R E N C E
D L
C O N T A I N E R S
& A M I s
E L A S T I C
K U B E R N E T E S
S E R V I C E
E L A S T I C
C O N T A I N E R
S E R V I C E
35. Modernize your contact center to improve customer service
P U T M L TO W O R K F O R Y O U R B U S I N E S S
conversational chat bots | call transcription | intelligent routing | sentiment analysis
VoC analytics text-to speech | multilingual omni-channel communication
POLLY TRANSCRIBE TRANSLATE COMPREHEND LEX
36. VOC analytics for customer service
Amazon
S3
Amazon
Athena
Amazon
Translate
Amazon
Comprehend
Amazon
Transcribe
sentiment, entities,
and key phrases
Call
Recordings
Transcribe and analyze historical call
recordings to measure customer
sentiment over time
• specific topics
• products and issues
38. Use AI services to strengthen safety and security
P U T M L TO W O R K F O R Y O U R B U S I N E S S
accurate facial analysis | identity protection | metadata extraction
REKOGNITION
IMAGE
COMPREHEND &
COMPREHEND
MEDICAL
REKOGNITION
VIDEO
40. Automate media workflows to reduce costs and monetize content
P U T M L TO W O R K F O R Y O U R B U S I N E S S
content moderation | contextual ad insertion | searchable media library
custom facial recognition | multi-language metadata search
REKOGNITION
IMAGE
REKOGNITION
VIDEO
COMPREHEND TRANSCRIBE TRANSLATE TEXTRACT
41. Monitoring radio & TV
content
“At Isentia, we enable customers to analyze and monitor
the media coverage for their brands. We create more than
13K summaries per day from radio and TV content. With
Amazon Transcribe, we can transcribe all the audio/video
content that we monitor and analyze the text data with
Amazon Comprehend. Features like timestamps and
punctuation make it very easy for us to search through the
data and drill down and present key insights for our
customers to review.”
Andrea Walsh - CIO, Isentia
42. Reduce localization costs and improve accuracy
P U T M L TO W O R K F O R Y O U R B U S I N E S S
custom vocabulary | timestamp generation | secure real-time translation | language identification
POLLY TRANSCRIBE TRANSLATE COMPREHEND
43. Scaling real-time
translation
Using Amazon Translate, Lionbridge is able to
scale machine translation in order to localize
content faster and in more languages. Using
Translate, Lionbridge was able to reduce
translation costs by 20 percent.
44. Accurately forecast future business outcomes
P U T M L TO W O R K F O R Y O U R B U S I N E S S
forecasting technology used by Amazon.com | multiple time-series data
forecast scheduling and visualization | supply chain integration
FORECAST
45. Personalize customer experiences with targeted recommendations
P U T M L TO W O R K F O R Y O U R B U S I N E S S
recommendation technology used by Amazon.com | context-aware recommendations
sentiment analysis | VoC analytics
PERSONALIZE REKOGNITION
IMAGE
REKOGNITION
VIDEO
COMPREHEND
47. Increase efficiency with automated document analysis
P U T M L TO W O R K F O R Y O U R B U S I N E S S
optical character recognition (OCR) | automatic data extraction | natural language processing
intelligent search | text analytics
TEXTRACT COMPREHEND &
COMPREHEND
MEDICAL
48. AI Services
Broadest and deepest set of capabilities
T H E AW S M L S TA C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Services
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
& C O M P R E H E N D
M E D I C A L
L E X F O R E C A S TR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T P E R S O N A L I Z E
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
FRAMEWORKS INTERFACES INFRASTRUCTURE
ML Frameworks + Infrastructure
F P G A SE C 2 P 3
& P 3 D N
E C 2 G 4
E C 2 C 5
I N F E R E N T I AG R E E N G R A S S E L A S T I C
I N F E R E N C E
D L
C O N T A I N E R S
& A M I s
E L A S T I C
K U B E R N E T E S
S E R V I C E
E L A S T I C
C O N T A I N E R
S E R V I C E
50. The path to ML-driven insights and predictions
While the power of ML is unrivaled,
“data scientists spend around 80% of
their time on preparing and
managing data for analysis” … hence
only 20% of their time is used to
derive insights
6–18
months
Fetch
data
Clean &
format
data
Prepare &
transform
data
Evaluate
model
Integrate
with prod
Monitor/
debug/
refresh
Train
model
51. Bringing machine learning to all developers
A M A Z O N S A G E M A K E R
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
52. Bringing machine learning to all developers
A M A Z O N S A G E M A K E R
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems
55. Bringing machine learning to all developers
A M A Z O N S A G E M A K E R
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
• K-Means Clustering
• Principal Component Analysis
• Neural Topic Modelling
• Factorization Machines
• Linear Learner (Regression)
• BlazingText
• Reinforcement learning
• XGBoost
• Topic Modeling (LDA)
• Image Classification
• Seq2Seq
• Linear Learner (Classification)
• DeepAR Forecasting
56. Over 200 algorithms and models
Natural Language
Processing
Grammar & Parsing Text OCR Computer Vision
Named Entity
Recognition
Video Classification
Speech Recognition Text-to-Speech Speaker Identification Text Classification 3D Images Anomaly Detection
Text Generation Object Detection Regression Text Clustering
Handwriting
Recognition
Ranking
A V A I L A B L E A L G O R I T H M S & M O D E L S
S E L E C T E D V E N D O R S
57. Bringing machine learning to all developers
A M A Z O N S A G E M A K E R
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
training
58. Bringing machine learning to all developers
A M A Z O N S A G E M A K E R
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
training Optimization
60. Bringing machine learning to all developers
A M A Z O N S A G E M A K E R
Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
training Optimization
One-click
deployment
61. Collect and
prepare
training data
Choose and
optimize your
ML algorithm
Set up and manage
environments
for training
Train and
tune model
(trial and error)
Deploy
model in
production
Scale and manage
the production
environment
Pre-built
notebooks for
common problems
Built-in, high
performance
algorithms
One-click
training Optimization
One-click
deployment
Fully managed with
auto-scaling, health checks,
automatic handling
of node failures,
and security checks
Bringing machine learning to all developers
A M A Z O N S A G E M A K E R
63. FRAMEWORKS INTERFACES INFRASTRUCTURE
AI Services
Broadest and deepest set of capabilities
T H E A W S M L S T A C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Services
ML Frameworks + Infrastructure
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
& C O M P R E H E N D
M E D I C A L
L E X F O R E C A S TR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T P E R S O N A L I Z E
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
F P G A SE C 2 P 3
& P 3 D N
E C 2 G 4
E C 2 C 5
I N F E R E N T I AG R E E N G R A S S E L A S T I C
I N F E R E N C E
D L
C O N T A I N E R S
& A M I s
E L A S T I C
K U B E R N E T E S
S E R V I C E
E L A S T I C
C O N T A I N E R
S E R V I C E
64. Introducing Reinforcement learning (RL)
Reinforcement learning
(RL)
Supervised learning
(ASR, computer vision)
Unsupervised learning
(Anomaly detection,
identifying text topics)
Amount of labeled training data required
Complexityofdecisions
66. Amazon SageMaker RL
Reinforcement learning for every developer and data scientist
Broad support
for frameworks
Broad support for simulation
environments
2D & 3D physics
environments and
OpenGym support
Support Amazon Sumerian,
AWS RoboMaker and the open
source Robotics Operating
System (ROS) project
Fully
managed
Example notebooks
and tutorials
K E Y F E A T U R E S
67. • Build machine learning models
in Amazon SageMaker
• Train, test, and iterate on the track using
the AWS DeepRacer 3D racing simulator
• Compete in the world’s first global autonomous
racing league, to race for prizes and a chance to
advance to win the coveted AWS DeepRacer Cup
A fully autonomous 1/18th-scale race car designed to help you
learn about reinforcement learning through autonomous driving
AW S D E E P R A C E R
68. The world’s first deep learning-
enabled video camera for developers
AW S D E E P L E N S
• Purpose-built for ML-skills development
• Fully programmable and customizable
• Build custom Amazon SageMaker models
• 10-minutes to your first deep learning project
69. H O W W E C A N H E L P
• Brainstorming
• Custom modeling
• Training
• Work side-by-side with Amazon experts
ML Solutions Lab
• Practical education on ML for new
and experienced practitioners
• Based on the same material used
to train Amazon developers
Machine Learning
Training and Certification