Introducing the new Amazon AI services (Rekognition, Polly, Lex) with some demos and some considerations on how to use machine learning engines (such as Apache MXNet) on AWS.
Artificial Intelligence & Deep Learning At Amazon Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Add ML-powered features to existing products Echo & Alexa
F1 Instances: Bringing Hardware Acceleration To All FPGA Images Available In AWS Marketplace F1 Instance With your custom logic running on an FPGA Develop, simulate, debug & compile your code Package as FPGA Images
Deep Learning Frameworks MXNet, Caffe, Tensorflow, Theano, Torch, CNTK and Keras Pre-installed components to speed productivity, such as Nvidia drivers, CUDA, cuDNN, Intel MKL-DNN with MXNet, Anaconda, Python 2 and 3 AWS Integration Deep Learning AMI
TEXT Market grew by > 20%. WORDS PHONEMES { { { { { ˈtwɛn.ti pɚ.ˈsɛnt ˈmɑɹ.kət ˈgɹu baɪ ˈmoʊɹ ˈðæn PROSODY CONTOUR UNIT SELECTION AND ADAPTATION TEXT PROCESSING PROSODY MODIFICATION STREAMING Market grew by more than twenty percent Speech units inventory
Amazon Lex: Speech Recognition & Natural Language Understanding Amazon Lex Automatic Speech Recognition Natural Language Understanding “What’s the weather forecast?” “It will be sunny and 25°C” Weather Forecast
Lex Bot Structure Utterances Spoken or typed phrases that invoke your intent BookHotel Intents An Intent performs an action in response to natural language user input Slots Slots are input data required to fulfill the intent Fulfillment Fulfillment mechanism for your intent
Hotel Booking City New York City Check In Nov 30th Check Out Dec 2nd Hotel Booking City New York City Check In Check Out “Book a Hotel” Book Hotel NYC “Book a Hotel in NYC” Automatic Speech Recognition Hotel Booking New York City Natural Language Understanding Intent/Slot Model Utterances “Your hotel is booked for Nov 30th” Polly Confirmation: “Your hotel is booked for Nov 30th” a in “Can I go ahead with the booking?”
Train model Evaluate and optimize Retrieve predictions Building smart applications with Amazon ML Create a datasource object pointing to your data Explore and understand your data Transform data and train your model 1 2 3
Train your model >>> import boto >>> ml = boto.connect_machinelearning() >>> model = ml.create_ml_model( ml_model_id=’my_model', ml_model_type='REGRESSION', training_data_source_id='my_datasource')
Train model Evaluate and optimize Retrieve predictions Building smart applications with Amazon ML Understand model quality Adjust model interpretation 1 2 3
Batch predictions Asynchronous, large-volume prediction generation Request through service console or API Best for applications that deal with batches of data records >>> import boto >>> ml = boto.connect_machinelearning() >>> model = ml.create_batch_prediction( batch_prediction_id = 'my_batch_prediction’ batch_prediction_data_source_id = ’my_datasource’ ml_model_id = ’my_model', output_uri = 's3://examplebucket/output/’)
Real-time predictions Synchronous, low-latency, high-throughput prediction generation Request through service API or server or mobile SDKs Best for interaction applications that deal with individual data records >>> import boto >>> ml = boto.connect_machinelearning() >>> ml.predict( ml_model_id=’my_model', predict_endpoint=’example_endpoint’, record={’key1':’value1’, ’key2':’value2’}) { 'Prediction': { 'predictedValue': 13.284348, 'details': { 'Algorithm': 'SGD', 'PredictiveModelType': 'REGRESSION’ } } }
Your Skill (Lambda function) Amazon Machine Learning get real-time predictions invoke Weather Forecast Historical Data get forecast build & train model
danilop
amixedcolor
akitotsukahara
tomokusaba
loic425
terhechte
pospome
star_zero
cocoeyes02
lhespanha
kazue
adammc331
chatwork_hr
ddemaree
samlambert
bluesmoon
reverentgeek
aleyda
bermonpainter
jeffersonlam
zakiyama
schacon
zakiwarfel
sonatard
caitiem20