Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Learning di AWS

Rendi unica l’offerta della tua startup sul mercato
con i servizi Machine Learning di AWS
Fausto Palma
AWS Solution Architect

© 2020, Amazon Web Services, Inc. or its Affiliates.
What is Machine Learning (ML)?
{ }
program
algorithm
training data
X
Y
input
data output
YX
trained
model
untrained
model
input
data
output
predictions
YX
inference
{ }
training
algorithm
training

What is Machine Learning (ML)?
“The field of study that gives computers the ability to
learn without being explicitly programmed”
(Arthur Samuel)
Machine lerner pioneer
training data
X
Y
trained
model
training
training data
X
environment
§ reinforcement learning
§ unsupervised
§ supervised

The reach of ML is growing

Machine Learning applications (examples)
Problem type Description
Ranking Helping users find the most relevant thing
Example
Ranking algorithm within Amazon Search

Ranking
Recommendation
Helping users find the most relevant thing
Giving users the thing they may be most
interested in
Example
Recommendations across the website
Amazon’s Choice

Ranking
Recommendation
Classification
interested in
Figuring out what kind of thing something is
Example
Product classification for our catalog
High-Low Dress Straight Dress
Striped Skirt Graphic Shirt

Ranking
Recommendation
Classification
Regression
interested in
Predicting a numerical value of a thing
Example
Predicting sales for specific ASINs
Seasonality | Out of stock | Promotions

Ranking
Recommendation
Classification
Regression
interested in
Clustering
Example
Putting similar things together
Close-matching for near-duplicates

Ranking
Recommendation
Classification
Regression
interested in
Finding uncommon things
Clustering
Example
Anomaly Detection
Putting similar things together
Fruit freshness
Good
Damage
Serious
Damage
Decay
Before
After

The AWS ML Stack
Broadest and most complete set of Machine Learning capabilities
VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS
Ground
Truth
ML
Marketplace
Neo
Augmented
AI
Built-in
algorithms
Notebooks Experiments
Model
training &
tuning
Debugger Autopilot
Model
hosting
Model Monitor
Deep Learning
AMIs & Containers
GPUs &
CPUs
Elastic
Inference
Inferentia FPGA
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Comprehend
+Medical
Amazon
Translate
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Fraud Detector
Amazon
CodeGuru
AI SERVICES
ML SERVICES
ML FRAMEWORKS & INFRASTRUCTURE
Amazon
Textract
Amazon
Kendra
Contact Lens
For Amazon Connect
SageMaker Studio IDE
Amazon SageMaker
DeepGraphLibrary

Amazon Rekognition
Sample APIs and resources Amazon Rekognition
(+ Custom Labels)
Amazon Rekognition Image
CompareFaces
CreateCollection
DeleteCollection
DeleteFaces
DescribeCollection
DetectFaces
DetectLabels
DetectModerationLabels
DetectText
GetCelebrityInfo
IndexFaces
ListCollections
ListFaces
RecognizeCelebrities
SearchFaces
SearchFacesByImage
Amazon Rekognition Custom
Labels
CreateProject
CreateProjectVersion
DescribeProjects
DescribeProjectVersions
DetectCustomLabels
StartProjectVersion
StopProjectVersion
Amazon Rekognition Video Stored Video
GetCelebrityRecognition
GetContentModeration
GetFaceDetection
GetFaceSearch
GetLabelDetection
GetPersonTracking
StartCelebrityRecognition
StartContentModeration
StartFaceDetection
StartFaceSearch
StartLabelDetection
StartPersonTracking
Amazon Rekognition Video Streaming
Video
CreateStreamProcessor
DeleteStreamProcessor
DescribeStreamProcessor
ListStreamProcessors
StartStreamProcessor
StopStreamProcessor
aws rekognition detect-labels –image
'{"S3Object":{"Bucket":"bucket","Name":"image"}}'
DetectLabels
Request:
{
"Image": {
"Bytes": blob,
"S3Object": {
"Bucket": "string",
"Name": "string",
"Version": "string"
}
},
"MaxLabels": number,
"MinConfidence": number
}
Response:
{
"LabelModelVersion": "string",
"Labels": [
{
"Confidence": number,
"Instances": [
{
"BoundingBox": {
"Height": number,
"Left": number,
"Top": number,
"Width": number
},
"Confidence": number
}
],
"Name": "string",
"Parents": [
{
"Name": "string"
}
]
}
],
"OrientationCorrection": "string"
}
Some sample calls…
Check the documentation:
https://docs.aws.amazon.com/rekognition/
AWS SDK available for:
C++, Go, Java, JavaScript,
.NET, Node.js, PHP,
Python, Ruby

Simple example with Amazon Rekognition
Lambda
Amazon
Rekognition
S3
Amazon SNS
picture email

Artificial Intelligence
S3:
Web UI
S3:
Media storage
Elasticsearch:
Search index
Amazon Rekognition Video:
Detect objects, scenes,
faces, & celebrities
AWS Elemental MediaConvert:
Transcode videos
Transcribe Comprehend
Lambda
API Gateway:
REST API
Lambda
Step Functions:
Orchestrate
analysis

Use Amazon SageMaker Studio to update models
and see impact on model quality

Sagemaker training process
S3 Bucket
with XY data
EC2
Instance
EBS
Volume
SageMaker
notebook
Model
saved in S3
model.fit()
SageMaker
service
1
EC2 EC22
Elastic
Container
Registry
3
Docker
Container
4
5
7
Trained
model
6

Training process on SageMaker
• Matrix factorization
• Regression
• Principal component analysis
• K-means clustering
• Gradient boosted trees
• And more!
17 Built-in algorithms
1
Bring your own script
(Amazon SageMaker managed container)
2
Bring your own
container
(you build the
Docker container)
3
Subscribe to
Algorithms and
Model Packages
on AWS
Marketplace
4

Bring your own script with PyTorch
1. Prepare your own script
- Configure script input parameters
- Build your model
- Setup data loaders and transformations
- Build your training loop
- Save the model
- Load the model for deploying
2. Create a sagemaker.pytorch.PyTorch estimator
3. Call the estimator’s fit method
4. Deploy an endpoint
5. Call the endpoint to get predictions
Pytorch
script file
SageMaker
notebook
https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#train-a-model-with-pytorch

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# https://github.com/pytorch/tutorials/blob/master/beginner_source/blitz/cifar10_tutorial.py#L118
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
Build your model

Artificial Neuron
x1
x2
x3
xn
…
w1
w2
w3
wn×
×
×
×
𝜎 y
∑ 𝜎
∑+
+
+
+
b
+

Artificial NN
x1
x2
x3
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
∑ 𝜎
y1
y2
y3

transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root=args.data_dir, train=True,
download=False, transform=transform)
train_loader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size,
shuffle=True, num_workers=args.workers)
testset = torchvision.datasets.CIFAR10(root=args.data_dir, train=False,
download=False, transform=transform)
test_loader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size,
shuffle=False, num_workers=args.workers)
Setup data loaders and transformations

Build your training loop
model = Net()
model = model.to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(0, args.epochs):
running_loss = 0.0
for i, data in enumerate(train_loader):
# get the inputs
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f’ % (epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0

Training loop
w11
w12
w13
w1n
w21
w22
w23
w2n
b1 b2
w31
w32
w33
w3n
b3
wm1
wm2
wm3
wmn
bm
… … … …
…
…
…
…
…
Y predictionX training Y training
≠
L loss function
Which direction and how
much to change parameters
to reduce the L function
∂L
∂w
∂L
∂w
×Learning Rate

model = Net()
model = model.to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
for epoch in range(0, args.epochs):
running_loss = 0.0
for i, data in enumerate(train_loader):
# get the inputs
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f’ % (epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
Build your training loop

import argparse
import os
if __name__ =='__main__':
parser = argparse.ArgumentParser()
# hyperparameters sent by the client are passed as command-line arguments to the script.
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--batch-size', type=int, default=64)
parser.add_argument('--learning-rate', type=float, default=0.05)
parser.add_argument('--use-cuda', type=bool, default=False)
# Data, model, and output directories
parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR’])
parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
parser.add_argument(‘--validation', type=str, default=os.environ['SM_CHANNEL_VALIDATION'])
parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])
args, _ = parser.parse_known_args()
# ... load from args.train, args.validation, and args.test, train a model, write model to args.model_dir.
Configure script input parameters

import argparse
import os
import torch
if __name__=='__main__’:
# default to the value in environment variable `SM_MODEL_DIR`. Using args makes the script more portable.
parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
args, _ = parser.parse_known_args()
# ... train `model`, then save it to `model_dir`
with open(os.path.join(args.model_dir, 'model.pth'), 'wb') as f:
torch.save(model.state_dict(), f)
Save the model
import os
import torch
def model_fn(model_dir):
model = Your_Model()
with open(os.path.join(model_dir, 'model.pth'), 'rb') as f:
model.load_state_dict(torch.load(f))
return model
Load the model for deploying

from sagemaker.pytorch import PyTorch
pytorch_estimator = PyTorch(entry_point='pytorch-train.py',
instance_type='ml.p2.xlarge',
instance_count=1,
framework_version='1.5.0',
py_version='py3',
hyperparameters = {'epochs': 20, 'batch-size': 64, 'learning-rate': 0.1})
2. Create a sagemaker.pytorch.PyTorch Estimator
3. Call the estimator’s fit method
pytorch_estimator.fit({'train': 's3://my-data-bucket/path/to/my/training/data’,
‘validation': 's3://my-data-bucket/path/to/my/validation/data',
'test': 's3://my-data-bucket/path/to/my/test/data’})
SageMaker
notebook

# Deploy my estimator to a SageMaker Endpoint and get a Predictor
predictor = pytorch_estimator.deploy(instance_type='ml.m5.xlarge',
initial_instance_count=1)
# `data` is a NumPy array or a Python list.
# `response` is a NumPy array.
response = predictor.predict(data)
4. Deploy an endpoint
5. Call the endpoint to get predictions
SageMaker
notebook

Hosting
Services
(endpoints)
Batch
Transform
Trained Model
Neo
Run
Anywhere DeepRacer
Amazon SageMaker Deployment Options
DeepLensMarketplace

Auto Scaling group
Availability Zone 1
Availability Zone 2
Availability Zone 3
Deployment / Hosting
Amazon SageMaker ML
Compute Instances
SageMaker Endpoints
Elastic
Load Balancing
Model
Endpoint
Amazon
API
Gateway
Input Data
(Request)
Prediction
(Response)
Client
Model
saved in S3
Elastic
Container
Registry
Trained
model
Inference
container

Train once and run anywhere with 2x performance
Amazon SageMaker Neo
Neo
Broad framework
support
Broad hardware
support
Open-source Neo-AI device runtime and compiler
1/10th the size of original frameworks

Wide selection of options for cost-effective inference
NETWORK ATTACHED
INFERENCE ACCELERATOR
eia1.medium
Mid-sized models, low-latency
budget with tolerance limits
ELASTIC INFERENCE
M5
Large models, high throughput,
and low-latency access to CUDA
GPU INSTANCES
P3 G4
Small models,
low throughput
CPU INSTANCES
C5
Inf1: High throughput, high performance,
and lowest cost in the cloud
CUSTOM CHIP
Inf1

Inf1 instances are built from
the ground up by AWS to
provide high performance,
cost-effective inference
https://aws.amazon.com/ec2/instance-types/inf1
HIGH PERFORMANCE LOW COST
AWS Custom 2nd Gen Intel
Xeon Scalable Processors
AWS Nitro AWS Inferentia
CUSTOM BUILT FOR
ML INFERENCE
100Gbps
Networking

AWS Training & Certification
https://www.aws.training: Free on-demand courses to help you build new cloud skills
Curriculum: Exploring the Machine Learning
Toolset
https://www.aws.training/Details/Curriculum?id=27155
Curriculum: Developing Machine Learning
Applications
Curriculum: Machine Learning Security
Curriculum: Demystifying AI/ML/DL
Video: AWS Foundations: Machine Learning
Basics
https://www.aws.training/Details/Video?id=49644
Curriculum: Conversation Primer: Machine
Learning Terminology
For more info on AWS T&C visit: https://aws.amazon.com/it/training/

Thanks!

Appendix – other useful links
https://sagemaker.readthedocs.io/en/stable/framework
s/pytorch/using_pytorch.html#train-a-model-with-
pytorch
Using PyTorch
script
documentation
Github Sagemaker
Examples
https://github.com/aws/amazon-sagemaker-examples
Github using
PyTorch script
example
https://github.com/aws/amazon-sagemaker-
examples/tree/master/sagemaker-python-
sdk/pytorch_cnn_cifar10
Note: there has been a recent update of the SageMaker SDK to version 2.0. Some examples are written for the previous SageMaker SDK. Please,
notice this link for further details:
https://sagemaker.readthedocs.io/en/stable/v2.html
You may downgrade temporarily with the following terminal command:
pip install sagemaker==1.72.0 –U
Upgrading to the latest SageMaker SDK can be done by executing:
pip install --upgrade sagemaker

Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Learning di AWS

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Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Learning di AWS