Opening Keynote

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Keynote Address
Dr. Werner Vogels
Vice President & CTO, Amazon.com

Image by Guido van Nispen in the Dutch Digital Pioneer collection
Dr. Werner Vogels
CTO Amazon.com
@werner

“The reality, of course, today is that if
you come up with a great idea you don't
get to go quickly to a successful product.
There's a lot of undifferentiated heavy
lifting that stands between your idea and
that success."
-Jeff Bezos

AWS has changed the cost of
starting a business radically

Investments by VCs have doubled
since the inception of AWS

More bets with
significantly higher payoffs

Capital shifts to later stages
with less risk.

Denni Gautama
VP of Engineering
Sergei Shvetsov
Head of Engineering,
Cloud Infrastructure

Keynote: AWS Startup Day Jakarta 2018
Denni Gautama - VP of Engineering, Travel Products
Sergei Shvetsov - Head of Engineering, Cloud Infrastructure

Raising a SEA Tech Unicorn,
a Traveloka Story
Denni Gautama
VP of Engineering, Travel Products

To enrich people’s lives by enabling them
to do more and go beyond traveling.
#EnablingMobility
#CreateMoments
Our mission

Traveloka goes
regional
~ 600 people
~ 100 engineers
Founded
as a
flight
meta-
search
Eight (8)
people
2012 2013 2014 2015
Pivot to
OTA
(Flight)
- Hotel
- Mobile
App
~ 200
people
2017
- Train, Attraction &
Activities, Prepaid top
up & data package,
Movies
- SG & IN Dev
Centers
~1,500 people
~300 engineers
2018
and beyond
- Eats, Car
Rental
- 40+ million
downloads
2,000+ people
~500+
engineers
Our Brief History
2016

Traveloka’s products and services
100+Airline
partners (FSC &
LCC)
450.000+ hotels
in 100 countries

Riding on AWS Platform
Sergei Shvetsov
Head of Engineering, Cloud Infrastructure

Benefits of AWS
• Bring new products to market faster than ever
• Do so much more, with a lot less effort
• Elasticity, scalability, reliability, security

September 2015 September 2018
AWS Services 9 37
EC2 390 1938
ELB 1 805
RDS 4 313
ElastiCache 1 283
Elasticsearch 0 51
How our Infra has grown

$5,000,000
Team of Engineers
Months of development
$5,000
An Engineer
Weeks of development
Launching an Idea
2005 2018

Cloudfront Lambda ECS Poly,
Rekognition,
Lex
RDS Route53 Elastic
Beanstalk
AWS Data
Pipeline
Kinesis
516
24 48 61 82
159
280
722
1,017
LAUNCHES
20 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6
1,400+
2 0 1 7
P A C E O F
I N N O V A T I O N
NEW CAPABILITIES DAILY
SageMaker,
Comprehend,
Fargate
Startups Have Evolved

Startup A
2010 AWS
Startup B
2018 AWS
A Tale Of Two Startups

1. Focused on releasing a MVP
2. Manual provisioning of servers
3. Manual deploy process
4. Limited monitoring
5. Averaging a deploy 1-2x a week
6. Dev on-boarding: 10-14 days
Startup A
2010 AWS
Idea / Early Stage
2010 Startup: Day One

1. Focused on productivity
2. IaC, Automation, Scalability
3. Deployment Pipelines
4. Improved Monitoring
5. Averaging a deploy: 2-3x a day
6. Engineering onboarding: 7 days
Stabilization Stage
2010 Startup: Year One
Startup A
2010 AWS

1. Focused on growth
2. Systems automated, auto scaling
3. Automated deploy processes
4. In-depth monitoring and alerting
5. Averaging a deploy: 15+ a day
6. Engineering onboarding: 1-3 Days
Growth
2010 Startup: Year Two
Startup A
2010 AWS

Engineers Deploys a Day Onboarding Time
Goal of Engineering Productivity

We’ve been working closely
with startups and enterprises
for 11 years.

1. Focused on product and growth
2. Systems automated, auto scaling
3. Automated deploy processes
4. In-depth monitoring and alerting
5. Engineering onboarding: 1-3 Day
Startup B
2018 AWS
2018 Startup: Day One

Real-time delivery of in-game
data, handling 45 billion
events per day.
Kinesis

P U S H I N G T H E B O U N D A R I E S O F S C A L E

V P C
GAME
Amazon
ECS
Amazon
Athena
Amazon
ELB
Amazon
EMR
Amazon
GuardDuty
Amazon
S3
Amazon
SNS
Amazon
SQS
AWS
Device
Farm
AWS
KMS
Amazon
Kinesis
PLATFORM ANALYTICS
Client Servers
Games
services
• Sta ts
• Profi l e
• C l ou d stora ge
• Ma tch making
Identity eCommerce
• Accou n t
• Pa rti es
• C h a t
• Pa ymen ts
• C a ta l og
• En ti tl emen ts
Amazon
Aurora
Epic’s architecture at a glance

R E A L - T I M E I N G E S T I O N D A T A W A R E H O U S E M A N A G E D C L U S T E R S
92M e ve n ts/ m in ute
~54B e ve n ts/ da y
40GB / m in ute o f da ta a t p e a k
5,000 Kin e sis sh a rds
14 p e ta b yte s
~2PB m o n th ly gro w th ra te
~8.3K b a tch ETL jo b s/ da y
22 EMR cluste rs in p ro d
+4K EC2 a n a lytics in sta n ce s
Analytics pipeline stats

Other
sources
APIs
Databases
N E A R R E A L T I M E P I P E L I N E
B A T C H P I P E L I N E
Hive
Game clients
Game servers
Launcher
Game services
A M A Z O N
K I N E S I S
Grafana
Scoreboards API
Limited Raw Data
(real time ad-hoc SQL)
Spark
streaming
on EMR
Tableau/BI
Ad-hoc SQL
select * from fortnite_base
where ...
A M A Z O N
D Y N A M O D B
( M e t r i c s t o r a g e )
User ETL
(Metri c defi n i ti on )
S 3
Batch ETL
on EMR
S 3
Analytics pipeline architecture

SE RVICE
HE A LTH
TO URNAME NTS BASIC
K PIs
GAME
A NA LYSIS
Using analytics to improve business outcomes

Object-level controlsUnmatched durability
availability, and
scalability
Best security,
compliance, and
audit capabilities
Business insights
into your data
Most ways to
bring data in
Twice as many
partner integrations
Amazon S3 Is The Most Popular
Choice For Data Lakes

COMPUTE
WRITE TIME READ TIME
41m 40s 13m 52s
S3
5 T B o f 2 M B o b j e c t s
w i t h 1 p r e f i x
S3 For Data Analytics
BEF O RE:

COMPUTE
12m 00s 7m 00s
w i t h 1 p r e f i x
S3
S3 Request Performance Increase
NO W :

COMPUTE
0h 1m 12s 0h 0m 42s
x10S3
P A RALLEL P RO C ESS ING:
a c r o s s 1 0 p r e f i x e s
S3 Request Performance Increase

AWS
GLUE
S t o r a g e
N o n - r e l a t i o n a l D a t a b a s e s
A U R O R A C O M M E R C I A L C O M M U N I T Y
R e l a t i o n a l D a t a b a s e s
Amazon DynamoDB Amazon ElastiCache
Amazon
Athena
Amazon EMR
Hadoop, Spark, Presto,
Pig, Hive…19 total
Amazon Redshift
+ Redshift Spectrum
Amazon
Elasticsearch
Service
Amazon
Kinesis
Amazon
QuickSight
A n a l y t i c s
Amazon
CloudSearch
AWS Data
Pipeline
M a c h i n e
L e a r n i n g
Amazon
S3
Amazon
EFS
Amazon
EBS
Amazon
Glacier
O n - P r e m i s e s
D a t a b a s e s
AWS Glue Helps You Get The Most Out Of Your Data

Put Machine Learning in the
hands of every developer

Personalized
recommendations
Inventing entirely new
customer experiences
Fulfillment automation
/ inventory management
Drones Voice driven
interactions
Machine Learning at Amazon: A long heritage

RETAIL
Demand Forecasting
Vendor Lead Time
Prediction
Pricing
Packaging
Substitute Prediction
CUSTOMERS
Recommendation
Product Search
Product Ads
Shopping Advice
Customer Problem
Detection
SELLER
Fraud Detection
Predictive Help
Seller Search & Crawling
CATALOGUE
Browse-Node Classification
Meta-data Validation
Review Analysis
Product Matching
TEXT
In-Book Search
Named-entity
Extraction
Summarization/X-ray
Plagiarism Detection
IMAGES
Visual Search
Product Image
Enhancement
Brand Tracking
THOUSANDS OF EMPLOYEES ACROSS THE COMPANY FOCUSED ON AI
Machine Learning at Amazon.com

COUNTERFEIT GOODS DETECTION
CUSTOMER ADOPTION MODELS
ITEM CLASSIFICATION
SALES LEAD RANKING
SEARCH INTENT
DEMAND ESTIMATION
CUSTOMER SUPPORT
DISPLAY ADS
The spark for hundreds of new smart
applications within Amazon.com

8 out of 10
of all ML workloads run on AWS
250%
growth YoY
10s of thousands
of active developers
running ML on AWS
TENSORFLOW ON AWS
Nucleus research, December 2017 - Report R206

Tens Of Thousands Of Customers
Running Machine Learning On AWS

“BMS understands the importance of technologies
like machine learning, which can further enable
efforts to discover, develop and deliver innovative
medicines that help patients prevail over serious
diseases. For example, our data scientists use AWS
tools like SageMaker to accelerate their modeling
work.”
– Paul von Autenried, CIO

Elevate the healthcare experience.
purpose
Simplifying access to healthcare.
mission

260 mio populations
The 4th most populous country
17,508 islands
The largest archipelago in the world
INDONESIA

74,093 villages
Large numbers of villages with poor
access to healthcare
Castrol Index #1 Jakarta
Urban cities access to healthcare impacted by
traffic issues
INDONESIA

Per 10,000 population
Physicians
World 13.9 / 10,000
UK 28.1 / 10,000
USA 24.5 / 10,000
Singapore 19.5 / 10,000
Malaysia 12.0 / 10,000
Thailand 3.9 / 10,000
Indonesia 3.0 / 10,000
Challenges that look particularly acute when
assessing healthcare.
INDONESIA

Cardiologist
1:260,000
1,000
Doctors
260
Million
Population
INDONESIA

Commute to
Hospital
Consultation
Payments
Administration
Waiting Room
Commute to Pharmacy
Receive Medicine
Commute to home
INDONESIA
Total time for traditional patient journey
4 HOURS

Traditional patient journey
4 HOURS
Patient at
Home
Find Doctor Consultation E-Prescription Payments Medicine Delivery
Halodoc patient journey
35 MINS
Halodoc Solving Pain

Apotik
Doctors
3.e-prescribe
3. e-prescribe
1. consultation and telemedicine
2. scheduling
3. e-prescribe
4. e-commerce on shared economy
3.e-prescribe
Lab
1. consultation and telemedicine
2. scheduling
The Ecosystem

One Platform –
All your healthcare needs
Contact Doctor
Pharmacy Delivery
Lab Service
Adherence
Articles
Cashless
eRx
Patient Medical
History
Patient Medical
History
Product Journey

Pharmacy
Delivery
Lab Service
Adherence
Articles
Cashless
eRx
Patient Medical History
Contact Doctor
Select your doctor & get into a consultation with the click of a button.
Chat, Call, Video - for you to directly consult with the doctor.
Get recommendation, prescription or referral as per your need.
Product Journey

Use the prescription coming from the online consultation or simply upload a photo of the
one you got from your doctor outside - to get medicines delivered.
Pharmacy
Delivery
Lab Service
Adherence
Articles
Cashless
Contact Doctor
eRx
Product Journey

Lab Service
Adherence
Articles
Cashless
Contact Doctor
eRx
Pharmacy
Delivery
Search for medicines you need, upload a prescription or browse for any health-care
products. Find exclusive deals. Pay by CoD or with the money in your HD Wallet.
Product Journey

Lab Service
Articles
Cashless
Contact Doctor
eRx
Pharmacy
Delivery
Got medicines to take or a prescription to adhere to -
with the click of a button, add reminders on Halodoc, to ensure you never miss a dose.
You can also view your adherence report, to track the progress & manage your condition better.
Adherence
Product Journey

Lab Service
Cashless
Contact Doctor
eRx
Pharmacy
Delivery
Adherence
Articles
Curious about a medical condition or just want to live a healthier life –
Articles on Halodoc has it all covered.
Get customized notification with content specifically designed for you & your enlightenment.
Product Journey

Cashless
Contact Doctor
eRx
Pharmacy
Delivery
Adherence
Articles
Lab Service
Specially curated test packages - all with the convenience of time & location, powered by
Prodia. Get your test results when they’re ready & view them on Halodoc.
Product Journey

Contact Doctor
eRx
Pharmacy
Delivery
Adherence
Articles
Lab Service
Link your insurance, & get direct benefits of your policy coverage on Halodoc. All without the
hassle of any wait or claim-filing.
Cashless
Product Journey

Contact Doctor
eRx
Pharmacy
Delivery
Adherence
Articles
Lab Service
Cashless
One Platform - All your healthcare needs.
All records in one place. For you & your family.
Your digital healthcare passport.
Product Journey

2Q2017 3Q2017 4Q2017 1Q2018 2Q2018 3Q2018E2Q2017 3Q2017 4Q2017 1Q2018 2Q2018 3Q2018E
Platform set for strong growth given rapid uptake in users completing consultations and returning
Monthly Consultations Completed (000) Average Daily Transactions
12,000+
GPs
8,000+
Specialists
50 In-House
Doctors for
Continuous
Availability
7.6x 7.5x
Teleconsultation

Strong traction in pharmacy delivery service given back-end integration with Go-Jek and Blibli
14,000+
SKUs
10,000+
Pharmacies
in Delivery
Network
30+ Cities and
Municipalities
served
Pharmacy Delivery
Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jun-18 Jul-18 Aug-18
6.2x 9x
2Q2017 3Q2017 4Q2017 1Q2018 2Q2018 3Q2018E
Transaction Volume (000) B2C GMV

AWS forms the plumbing of “health care data network” including 1000+ pharmacies,
hundreds of hospitals and thousands of Doctor partners.
Reliable Partner
• Faster time to market, critical to us as a start up
• Reduced time maintaining Infra, allowing us to focus on
features user love.
• Scalability made easy, allowing for the strong month
Halodoc is “all-in” AWS on cloud.

Overall Architecture
Components
1. ALBs for Load Distribution
2. EC2 instances hosting the application
3. RDS MySQL instances for relational
database storage.
4. DynamoDB for key-value data storage
a. Server event pipeline (detailed later)
for async backup of key-value data to
relational store
5. S3 for long term data archival and storage.
6. Athena/Redshift for data query and
analysis

Sample - Server Events System
SNS based pipeline
DynamoDB to store event
configurations.
1 SNS topic per event type generated.
Multiple consumers for each SNS topic.
Kinesis Firehose delivery Streams for
S3 delivery.
Kinesis based pipeline
DynamoDB to store event
configurations.
Multiple Kinesis streams per event type
generated.
Lambda and Firehose streams as
consumers for streams.
Lambda for record transformation and
S3 delivery.

Mensa 2 Building, HR Rasuna Said B34, Jakarta 12940 – Indonesia
www.halodoc.com

ML PLATFORMS
A M A Z O N
S A G E M A K E R
A W S
D E E P L E N S
ML FRAMEWORKS

Train with
SageMaker
algorithms
Train with
your own
algorithms
Train with
TensorFlow
and MXNet
A/B testOptimize
your models
Host models
Amazon SageMaker: Custom ML Models Using Your Own Data

Amazon SageMaker: Built-In Algorithms Designed For “Infinite Scale”
Object detection
Multi-class classifiers
Deep forecasting
Text classification
Anomaly detection
PCA, kNN, regression, boosted trees…

Object detection
Multi-class classifiers
Deep forecasting
Text classification
Anomaly detection
PCA, kNN, regression, boosted trees…
Designed to
be 10X better
Streaming
data
Single pass
training
Checkpointing Available
as an API
10x API
Amazon SageMaker: Built-In Algorithms Designed For “Infinite Scale”

“How can we make our algorithms as fast as those in SageMaker?”

ACCELERATE YOUR OWN ALGORITHMS BY STREAMING LARGE
VOLUMES OF TRAINING DATA FROM AMAZON S3
A m a z o n S a g e M a k e r
S t r e a m i n g A l g o r i t h m s

SageMaker Streaming For Custom Algorithms
Faster
training
Stream data to
your own algorithm
Quicker time to
start training
Lower cost
training

Faster
training
Stream data to
your own algorithm
Quicker time to
start training
Additional frameworks
coming soon
Lower cost
training
TensorFlow
SageMaker Streaming For Custom Algorithms

A/B testOptimize
your models
Host modelsTrain with
SageMaker
algorithms
Train with
your own
algorithms
Train with
TensorFlow
and MXNet
Amazon SageMaker: Custom ML
Models Using Your Own Data

Low latency
Easy to integrate
API endpoint
Auto-scaling
Fault tolerant,
multi-AZ
Amazon SageMaker: Elastic
Hosting For Custom Models

Low latency
Easy to integrate
API endpoint
Auto-scaling
Fault tolerant,
multi-AZ
What if your
files are big?
What if you need
to batch process?
But…
Amazon SageMaker: Elastic
Hosting For Custom Models

RUN FULLY MANAGED, HIGH -THROUGHPUT
BATCH TRANSFORM JOBS WITH A SIMPLE API CALL
A m a z o n S a g e M a k e r
B a t c h T r a n s f o r m

Batch test models
before hosting
Process data dumps
in a batch
Process large files
more easily
Test models
before deployment
at the edge
Batch Processing For Amazon SageMaker

Batch test models
before hosting
Process data dumps
in a batch
Process large files
more easily
Test models
before deployment
at the edge
Reuse pre-processing
pipelines between
training and prediction
Use same model
for batch and
real-time
Fully managed
batch transforms
Batch Processing For Amazon SageMaker

AI SERVICES
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
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 L E X
Vision Speech Language
Chatbots &
Contact Centers
ML PLATFORMS
A M A Z O N
S A G E M A K E R
A W S
D E E P L E N S
ML FRAMEWORKS

A m a z o n
L e x
A m a z o n
T r a n s c r i b e
A m a z o n
C o m p r e h e n d
TR A N S CR I P T
A m a z o n
C o n n e c t
Analytics
Improving Contact Centers With Artificial Intelligence

Improving Contact Centers With Artificial Intelligence
A m a z o n
L e x
A m a z o n
T r a n s c r i b e
A m a z o n
C o m p r e h e n d
TR A N S CR I P T
A m a z o n
C o n n e c t
Analytics

FULLY AUTOMATED MULTI-CHANNEL AUDIO TRANSCRIPTION
C h a n n e l S y n t h e s i s F o r
A m a z o n T r a n s c r i b e

A M A Z O N
T R A N S C R I B E
C u s t o m e r
A g e n t
T R A N S C R I B E
T R A N S C R I B E
S y n c h r o n i z e
T r a n s c r i p t s
Si ngl e
Transc ri p t
Channel Synthesis For Amazon Transcribe

Improving Voice Of Customer Analytics
With Artificial Intelligence
A M A Z O N
C O M P R E H E N D
A M A Z O N T R A N S L A T E A M A Z O N S 3
A M A Z O N
Q U I C K S I G H T
A M A Z O N
A T H E N A
S e n t i m e n t , e n t i t i e s ,
k e y p h r a s e s
S O C I A L
M E D I A
a n d
S U P P O R T
C H A N N E L S

JAPANESE, RUSSIAN, ITALIAN, CHINESE
(TRADITIONAL), TURKISH, AND CZECH
Amazon Translate:
12 New Language Pairs
C O M I N G S O O N
DUTCH, SWEDISH, POLISH,
DANISH, HEBREW, AND FINNISH
12 more!

Improving Voice Of Customer Analytics
With Artificial Intelligence
A M A Z O N
C O M P R E H E N D
A M A Z O N T R A N S L A T E A M A Z O N S 3
A M A Z O N
Q U I C K S I G H T
A M A Z O N
A T H E N A
S e n t i m e n t , e n t i t i e s ,
k e y p h r a s e s
S O C I A L
M E D I A
a n d
S U P P O R T
C H A N N E L S
24 language pairs

FINE-GRAINED TEXT ANALYSIS
Amazon Comprehend
Syntax Identification

"I love my yellow
Fire Tablet”
P h i l ’ s R e v i e w s
Amazon Comprehend Syntax Identification

"I love my yellow
Fire Tablet”
Sentiment: positive
Entity: Fire Tablet

Kindle Fire
POSITIVE NEGATIVE
"I love my yellow
Fire Tablet”
Sentiment: positive
Entity: Fire Tablet

Sentiment: positive
Entity: Fire Tablet
Adjective: Yellow
"I love my yellow
Fire Tablet”

Kindle Fire: Positive
Sentiment: positive
Entity: Fire Tablet
Adjective: Yellow
Canary
Yellow
Black Punch
Red
Marine
Blue
"I love my yellow
Fire Tablet”

AWS has shipped over 100
new services and major new
features for machine
learning since re:Invent 2017
A m a z o n P o l l y W o r d P r e s s p l u g i n i n t e g r a t e s T r a n s l a t e A m a z o n R e k o g n i t i o n I m a g e a n d V i d e o l a u n c h i n T o k y o a n d S y d n e y A m a z o n T r a n s c r i b e - G A A m a z o n T r a n s l a t e - G A A n o m a l y D e t e c t i o n ( R a n d o m C u t F o r e s t ) A l g o r i t h m
A u t o m a t e r o l e c r e a t i o n d u r i n g s e t u p A u t o m a t i c M o d e l T u n i n g A u t o s c a l i n g c o n s o l e A W S D e e p L e n s d e v i c e s a v a i l a b l e f o r s a l e B l a z i n g T e x t A l g o r i t h m B u i l t - i n A l g o r i t h m s P i p e M o d e S u p p o r t C a f f e t o M X N e t c o d e t r a n s l a t o r
C h a i n e r p r e - b u i l t c o n t a i n e r C l o u d F o r m a t i o n s u p p o r t C l o u d T r a i l i n t e g r a t i o n f o r a u d i t l o g s C U D A 9 . 0 a n d 9 . 1 + c u D N N 7 . 1 . 4 + G P U D r i v e r 3 9 0 . 4 6 + N C C L 2 . 2 . 1 3 u p g r a d e C u s t o m e r V P C s u p p o r t f o r t r a i n i n g a n d h o s t i n g D C A : A W S D e e p L e a r n i n g A M I i n D C A
D e e p A R a l g o r i t h m D e e p L e a r n i n g A M I i n G o v C l o u d D e e p L e a r n i n g A M I : A d d C h a i n e r , M X N e t 1 . 1 s u p p o r t ( i n c l . B J S ) D e e p L e a r n i n g A M I : E C 2 O p t i m i z e d B i n a r i e s f o r T e n s o r F l o w D e e p L e a r n i n g A M I : O p t i m i z e d C h a i n e r 4 , C N T K 2 . 5
D e e p L e a r n i n g A M I : R e f r e s h f o r E C 2 o p t i m i z e d T e n s o r F l o w 1 . 8 D e e p L e a r n i n g A M I : R e f r e s h w i t h T F 1 . 5 a n d M X N e t 1 . 1 D e e p L e a r n i n g A M I : R e g i o n a l E x p a n s i o n i n 5 p u b l i c r e g i o n s : U S W e s t ( C a l i f o r n i a ) , C a n a d a , S a o P a u l o , L o n d o n , a n d P a r i s
D e e p L e a r n i n g A M I : T e n s o r F l o w m u l t i - g p u o p t i m i z a t i o n s u s i n g H o r o v o d , N C C L a n d O p e n M P ID e e p L e a r n i n g A M I : T F 1 . 5 R C 0 , M M S , T F S e r v i n g , T e n s o r B o a r d ( i n c l u d i n g B J S )F P 1 6 s u p p o r t
D e e p L e n s i n t e g r a t i o n w i t h A m a z o n K i n e s i s v i d e o s t r e a m E x p o r t / I m p o r t A m a z o n L e x b o t s c h e m a
F a c e R e c o g n i t i o n v 3 l a u n c h G D P R C o m p l i a n c e G r a d i e n t C o m p r e s s i o n
I m p o r t f r o m S a g e M a k e r t o D e e p L e n s I n c r e a s e C h a r a c t e r L i m i t I n t e r n e t - f r e e n o t e b o o k i n s t a n c e s K M S s u p p o r t f o r t r a i n i n g a n d h o s t i n g L i n e a r L e a r n e r I m p r o v e m e n t s - E a r l y S t o p p i n g , N e w L o s s F u n c t i o n s , a n d C l a s s W e i g h t s m l . p 3 . 2 x l a r g e n o t e b o o k i n s t a n c e s
M o d e l O p t i m i z e r M o r e i n s t a n c e t y p e s s u p p o r t M X N e t M o d e l S e r v e r M X N e t M o d e l S e r v e r M X N e t M o d e l S e r v e r v 0 . 2 M X N e t M o d e l S e r v e r v 0 . 3 M X N e t v 1 . 0 r e l e a s e M X N e t v 2 . 0 r e l e a s e n b e x a m p l e s u p p o r t i n S a g e M a k e r n o t e b o o k i n s t a n c e s
A m a z o n L e x : D T M F S u p p o r t N E R , K e y p h r a s e , S e n t i m e n t B a t c h N e w B r e a t h S S M L t a g N e w F r e n c h V o i c e ( f e m a l e ) N e w P h o n a t i o n S S M L t a g N o t e b o o k b o o t s t r a p s c r i p t O N N X v 1 . 0 a n n o u n c e m e n t P C I D S S C o m p l i a n c e P o l l y i n G o v C l o u d P o l l y v o i c e s i n A l e x a S k i l l s
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S a g e M a k e r r e g i o n e x p a n s i o n t o I C N S t o r e t r a n s c r i p t i o n o u t p u t i n c u s t o m e r S 3 b u c k e t s S u p p o r t f o r K e r a s 2 . 0 S u p p o r t G l u o n m o d e l s T a g - b a s e d a c c e s s c o n t r o l T e n s o r F l o w 1 . 5 , M X N e t 1 . 0 , a n d C U D A 9 S u p p o r t T e n s o r F l o w 1 . 6 a n d M X N e t 1 . 1 C o n t a i n e r s
T e n s o r F l o w 1 . 7 C o n t a i n e r s T e n s o r F l o w 1 . 8 C o n t a i n e r T e n s o r f l o w a n d C a f f e s u p p o r t T e n s o r F l o w a n d M X N e t C o n t a i n e r s - O p e n S o u r c i n g a n d L o c a l M o d e T r a i n i n g J o b c l o n i n g i n c o n s o l e T r a n s c r i b e i n t e g r a t i o n w i t h C l o u d T r a i l T r a n s c r i b e i n t e g r a t i o n w i t h C l o u d W a t c h
D L A M I r e f r e s h w i t h M X N e t 1 . 0 , T F f o r C U D A 9 a n d P y T o r c h f o r C U D A 9 H I P A A c o m p l i a n c eT r a n s l a t e - - a d d t o P o l l y W o r d P r e s s p l u g i n T r a n s l a t e - - G D P R c o m p l i a n c e
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