聯繫銷售: https://aws.amazon.com/tw/contact-us/ 與銷售線上聊天: https://pages.awscloud.com/tw-hkt-sales-chat.html 發展機器學習模型需要長期大量投入、因應數據分布的更動重複進行。本工作坊將帶您了解自動化機器學習的最新概念，包含如何應用發展、且逐漸完成健全的模型。 您將學習到如何利用Amazon SageMaker GroundTruth來運行客製標籤作業，建立更大的資料組來優化微調模型。您也會學習到用SageMaker的自動模型優化調整參數，掌握貝氏最佳化 (Bayesian Optimization) 產出最準確、快速、最佳化的結果。

1. S U M M I T
Taipei

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https://bit.ly/2MxHNLB

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Automatic Labelling and Model
Tuning with Amazon
SageMaker
Adam Lynch
Partner Solutions Architect
Amazon Web Services
S e s s i o n I D

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Agenda
Introduction – 15 min
Overview of Amazon SageMaker GroundTruth – 15 min
Lab Automatic Labelling with Amazon SageMaker GroundTruth – 45 min
Working Lunch – 15 min
Lab Private Workforce Labelling – 30 min
Overview of Model Tuning using Bayesian Optimisation – 15 min
Lab Automatic Model Tuning with Amazon SageMaker – 45 min

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AWS Account for the Labs
In order to complete this workshop you'll need an AWS Account
with admin access
There are resources required by this workshop that are eligible for
the AWS free tier if your account is less than 12 months old.
And we will supply some credits for other tasks.

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What data science tasks can be automated?

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Data science tasks

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AutoML – definition and goals
AutoML aims to maximise the performace of machine learning programs
without human assistance and subject to a computational budget.
Core goals:
a) Good performance: good generalization performance across various
input data and learning tasks can be achieved.
b) No assistance from humans: configurations can be automatically done
for machine learning tools.
c) High computational efficiency: the program can return a reasonable
output within a limited budget.
Taking the Human out of Learning Applications:
A Survey on Automated Machine Learning
Quanming Yao et al. arXiv:
1810.13306v3 [cs.AI] 17 Jan 2019

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The challenges of AutoML adoption
Deep Learning Human Design Computational
Budget

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Some tough challenges in machine learning
Availability of Labelled Data The Road Not Travelled Drift

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Promising approaches
Learning to Learn Simulation Reinforcement Learning

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KERAS
AI SERVICES
ML SERVICES
ML FRAMEWORKS +
INFRASTRUCTURE C5 C5n
P3 P3dn
Vision Speech Languages Chatbots Vertical
AMAZON
SAGEMAKER
AMAZON SAGEMAKER
GROUND TRUTH
AMAZON
SAGEMAKER RL
AWS MARKETPLACE
FOR ML
AMAZON
SAGEMAKER NEO
AWS
DEEPRACER
AWS
DEEPLENS
AMAZON ELASTIC INFERENCE
AWS INFERENTIA
AWS GREENGRASS
AMAZON
PERSONALIZE
AMAZON
FORECAST
AMAZON
TEXTRACT
AMAZON
REKOGNITION
AMAZON
LEX
AMAZON
POLLY
AMAZON
TRANSCRIBE
AMAZON
COMPREHEND
AMAZON
TRANSLATE
N E W
N E W
N E W

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Data labeling and machine learning
Labeled data
Model
training
Prepare and
label data
Inference

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Data labeling is hard…
Need to label large datasets
Requires humans to perform labeling
Becomes time consuming and costly
Difficult to achieve high accuracy for
labels

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Amazon SageMaker: Build, train, and deploy ML
1
2
3
1
2
3

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K E Y F E A T U R E S
Automatic labeling via
machine learning
Ready-made and
custom workflows
Label
management
Private and public
human workforce
Amazon SageMaker Ground Truth
Label machine learning training data easily and
accurately

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Amazon SageMaker Ground Truth
How it works

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Amazon SageMaker Ground Truth
How it works

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Amazon SageMaker Ground Truth
How it works

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Amazon SageMaker Ground Truth
How it works

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Amazon SageMaker Ground Truth
How it works

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Step 1: Create a Ground Truth labeling job

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Step 2: Provide details for a labeling job

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Step 2: Provide details for a labeling job

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Step 2: Provide details for a labeling job

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Labeling job is now running

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Labeling app: Human workers label the
images

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AWS Management Console: View labels for
images

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AWS Management Console: View labels

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Solving the following problem:
𝑚𝑎𝑥 𝑥 ∈ 𝐴 𝑓(𝑥)

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Defining ‘A’ using Amazon SageMaker
"ParameterRanges": {
"CategoricalParameterRanges":
[
{ "Name": "tree_method",
"Values": ["auto", "exact", "approx", "hist"]}
],
"ContinuousParameterRanges":
[
{ "Name": "eta", "MaxValue" : "0.5", "MinValue": "0" }
],
"IntegerParameterRanges":
[
{ "Name": "max_depth", "MaxValue": "10", "MinValue": "1", }
]
}

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On Amazon Sagemaker – configure the tuning
job
tuning_job_config = {
"ParameterRanges": {…}
"ResourceLimits": { "MaxNumberOfTrainingJobs": 20, "MaxParallelTrainingJobs": 3 },
"Strategy": "Bayesian",
"HyperParameterTuningJobObjective": {
"MetricName": "validation:auc",
"Type": "Maximize"
}
}

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Launch the tuning job
tuning_job = smclient.create_hyper_parameter_tuning_job(
HyperParameterTuningJobName = "MyTuningJob“,
HyperParameterTuningJobConfig = tuning_job_config,
TrainingJobDefinition = training_job_definition)

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42. Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Adam Lynch
atlynch@amazon.com