- Deep learning and machine learning techniques can be used for image pattern analysis, speech recognition, natural language processing, and autonomous vehicles.
- Amazon Web Services provides services and capabilities to help customers build, train, and deploy deep learning and AI models at scale.
28. • A Kumar, et al, Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding,
https://arxiv.org/abs/1711.00549
• R Maas, et al, Domain-Specific Utterance End-Point Detection for Speech Recognition - Proc. Interspeech 2017,
http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1673.PDF
• B King et al, Robust Speech Recognition Via Anchor Word Representations - Proc. Interspeech 2017,
http://www.isca-speech.org/archive/Interspeech_2017/pdfs/1570.PDF
• A Kumar et al, Zero-shot learning across heterogeneous overlapping domains - Proc. Interspeech 2017,
http://www.isca-speech.org/archive/Interspeech_2017/pdfs/0516.PDF
• M Sun et al, Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting,
Spoken Language Technology Workshop (SLT), 2016 IEEE
• F Ladhak et al, LatticeRnn: Recurrent Neural Networks Over Lattices - Proc. Interspeech 2016, http://www.isca-
speech.org/archive/Interspeech_2016/pdfs/1583.PDF
• S Panchapagesan et al, Multi-Task Learning and Weighted Cross-Entropy for DNN-Based Keyword Spotting -
Proc. Interspeech 2016, http://www.isca-speech.org/archive/Interspeech_2016/pdfs/1485.PDF
• R Maas et al, Anchored Speech Detection - Proc. Interspeech 2016, http://www.isca-
speech.org/archive/Interspeech_2016/pdfs/1346.PDF
• M Sun et al, Model Shrinking for Embedded Keyword Spotting, 2015 IEEE 14th International Conference on
Machine Learning and Applications (ICMLA)
• N Strom, Scalable distributed DNN training using commodity GPU cloud computing, Annual Conference of the
International Speech Communication Association 2015, http://www.isca-
speech.org/archive/interspeech_2015/papers/i15_1488.pdf
29. NEW!
“Alexa, start the meeting.”
“Alexa, dial 555-8000.”
“Alexa, lower the blinds.”
“Alexa, ask Salesforce which
big deals closed today.”
35. - -
FRAMEWORKS AND INTERFACES
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
VISION
AWS DeepLensAmazon SageMaker
LANGUAGE
Amazon Rekognition Amazon Polly Amazon Lex
Amazon Rekognition Video Amazon Transcribe Amazon Comprehend
Alexa for Business
VR/AR
Amazon Sumerian
APPLICATION SERVICES
Amazon Machine Learning Amazon EMR & SparkMechanical Turk
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
Amazon Translate
36. F R A M E W O R K S A N D I N T E R FA C E S
NVIDIA
Tesla V100 GPUs
P3 1 Petaflop of compute
NVLink 2.0
5,120 Tensor cores
128GB of memory
~14X faster than P2
P3 Instance Deep Learning AMI Frameworks
PLATFORM SERVICES
VISION LANGUAGE VR/IR
APPLICATION SERVICE
AWS DeepLensAmazon SageMaker Amazon Machine Learning Amazon EMR & SparkMechanical Turk
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
37. 2 0 3
p3.2xlarge
= $5 per hour
(서울 리전 기준)
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= $100 per hour
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43. FRAMEWORKS AND INTERFACES
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
VISION
AWS DeepLensAmazon SageMaker
LANGUAGE
Amazon Rekognition Amazon Polly Amazon Lex
Amazon Rekognition Video Amazon Transcribe Amazon Comprehend
Alexa for Business
VR/AR
Amazon Sumerian
APPLICATION SERVICES
Amazon Machine Learning Amazon EMR & SparkMechanical Turk
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
Amazon Translate
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Regression
Linear Learner Supervised
XGBoost Algorithm Supervised
Discrete Recommendations Factorization Machines Supervised
Image Classification Image Classification Algorithm Supervised, CNN
Neural Machine Translation Sequence to Sequence Supervised, seq2seq
Time-series Prediction DeepAR Supervised, RNN
Discrete Groupings K-Means Algorithm Unsupervised
Dimensionality Reduction PCA (Principal Component Analysis) Unsupervised
Topic Determination Latent Dirichlet Allocation (LDA) Unsupervised
Neural Topic Model (NTM) Unsupervised,
Neural Network Based
45. CA
“With Amazon SageMaker, we can accelerate our Artificial Intelligence
initiatives at scale by building and deploying our algorithms on the
platform. We will create novel large-scale machine learning and AI
algorithms and deploy them on this platform to solve complex problems
that can power prosperity for our customers."
- Ashok Srivastava, Chief Data Officer, Intuit
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Micro-SD
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USB
USB
Reset
Audio out
Power
• Intel Atom Processor
• Intel Gen9 graphics
• Ubuntu OS- 16.04 LTS
• 100 GFLOPS performance
• Dual band Wi-Fi
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resolution
• 2 USB ports
• Micro HDMI
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• AWS Greengrass
• clDNN Optimized for MXNet
47. FRAMEWORKS AND INTERFACES
AWS DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
AWS DeepLensAmazon SageMaker Amazon Machine Learning Amazon EMR & SparkMechanical Turk
INSTANCES
GPU (G2/P2/P3) CPU (C5) FPGA (F1)
VISION LANGUAGE
Amazon Rekognition
Image
Amazon
Polly
Amazon
Lex
Amazon Rekognition
Video
Amazon
Transcribe
Amazon
Comprehend
Alexa for
Business
VR/AR
Amazon
Sumerian
APPLICATION SERVICES
Amazon
Translate
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53. AWS ML Customers
APPLICATION SERVICES
Amazon Lex
Amazon Polly
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Rekognition Image
Amazon Rekognition Video
PLATFORM SERVICES
Amazon SageMaker AWS DeepLens
FRAMEWORKS AND INTERFACES
AWS Deep Learning AMI
Apache MXNet
Caffe2
CNTK
PyTorch
TensorFlow
Theano
Torch
Gluon
Keras
AWS ML Platform
DATA LAKE STORAGE
Amazon S3
SECURITY
Access Control
Encryption
COMPUTE
Powerful GPU and CPU Instances
ANALYTICS
Amazon Athena
Amazon Redshift
and Redshift Spectrum
Amazon EMR
(Spark, Hive, Presto, Pig)
AWS Glue
Amazon Kinesis
Amazon QuickSight
Amazon Macie
AWS Organizations
AWS Cloud Platform
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