At OSCON 2018, Adrian Cockcroft detailed the many ways AWS participates in open source: contributing to open source projects, reporting bugs, contributing fixes and enhancements to a wide spectrum of projects ranging from the Linux kernel to PostgreSQL and Kubernetes, and managing the hundreds of projects of its own.
14. F R A M E W O R K S
KERAS
P L A T F O R M S
A M A Z O N
S A G E M A K E R
15. F R A M E W O R K S
KERAS
P L A T F O R M S
A M A Z O N
S A G E M A K E R
A P P L I C A T I O N S E R V I C E S
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
16. F R A M E W O R K S
KERAS
P L A T F O R M S
A M A Z O N
S A G E M A K E R
A P P L I C A T I O N S E R V I C E S
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
17. KERAS
O P E N S O U R C E F R A M E W O R K S A N D I N T E R F A C E S
F r a m e w o r k s I n t e r f a c e s
Complete control over the entire stack
18. KERAS
O P E N S O U R C E F R A M E W O R K S A N D I N T E R F A C E S
Complete control over the entire stack
F r a m e w o r k s I n t e r f a c e s
NVIDIA
Tesla V100 GPUs
(14x faster than P2)
P3
Open Source
Machine Learning AMIs
5,120 Tensor cores
128GB of memory
1 Petaflop of compute
NVLink 2.0
I N F R A S T R U C T U R E
21. Where do we spend our time?
B UI L D TRAI N D EP L O Y
22. A P P L I C A T I O N S E R V I C E S
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
F R A M E W O R K S
KERAS
P L A T F O R M S
A M A Z O N
S A G E M A K E R
25. Amazon SageMaker
Fully managed
hosting with
auto-scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
B UI L D TRAI N D EP L O Y
29. 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
Amazon SageMaker
30. Put open source machine
learning in the hands of
every developer and data
scientist
M L @ A W S
O U R
M I S S I O N
31. B R I N G I N G C L O U D S C A L E T O
D a t a b a s e s
32. M i g ra t e b e t w e e n
o n -p re m a n d A W S
M i g ra t e b e t w e e n
d a t a b a s e s
A u t o m a t e d s c h e m a
c o n v e rs i o n
D a t a re p l i c a t i o n fo r
z e ro d o w n t i m e
AWS Database Migration Service
Helps convert from proprietary enterprise databases to open source
76,000+ unique databases
migrated using DMS
33. Amazon Aurora
The fastest-growing
service in AWS history
MySQL and PostgreSQL compatible
Several times faster than standard MySQL
and PostgreSQL
Highly available and durable
1/10th the cost of commercial grade
database
36. EKS
Platform for enterprises to
run production-grade
Kubernetes
M a n a g e d a n d
c o n s i s t e n t e x p e r i e n c e
S e a m l e s s , n a t i v e
i n t e g r a t i o n
w i t h A W S s e r v i c e s
B u i l t w i t h t h e
O S S c o m m u n i t y
U p s t r e a m a n d
C e r t i f i e d