Track 6 Session 3_如何藉由 AWS AI 和機器學習平台搭建多功能的 AI 解決方案.pptx

1. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. 如何藉由 AWSAI 和機器學習平台搭建多功能的AI 解決方案 T r a c k 6 | S e s s i o n 3 Ivan Cheng (鄭志帆) Manager, Solutions Architecture Amazon Web Services
2. $50 B By 2021, global spending on AI and cognitive technologies will exceed $50 billion Source: IDC, 2018 InnovationDecision making Customer experience Business operations Competitive advantage Centerpiecefor digitaltransformation
3. Our mission at AWS Put machine learning (ML) in the hands of every developer
4. 200+ new features and services launched in the last year Broadest and deepest set of AI and ML services Up to 70% cost reduction in data labeling Up to 10x faster performance Up to 75% lower inference cost Reduced deployment time Accelerate your adoption of ML with Amazon SageMaker Built on the most comprehensive cloud platform optimized for ML AWS named as a leader in Gartner’s Infrastructure as a Service (IaaS) Magic Quadrant for the 9th consecutive year WhyAWS for ML?
5. 10,000+ customers | More ML happens on AWS than anywhere else MLishappeningincompaniesofeverysizeandineveryindustry
6. Technology Bringing AI into your digital transformation requires a new stack that makes it easier to put ML to work
7. Vision Speech Text Search Chatbots Personalization Forecasting Fraud Development Contact centers Ground Truth Augmented AI ML Marketplace Neo Built-in algorithms Notebooks Experiments Model training & tuning Debugger Autopilot Model hosting Model Monitor Deep learning AMIs & containers GPUs & CPUs Amazon Elastic Inference AWS 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 Amazon SageMaker Studio IDE NEW NEW! NEW! NEW! NEW! NEW! NEW! NEW! NEW! NEW! NEW! Amazon SageMaker TheAWS MLstack Broadest and most complete set of ML capabilities
9. TheMLworkflowis iterativeand complex Bringing ML to all developers Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models
10. Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 AmazonSageMakerhelps you build,train,and deploy models Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models
11. Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 Fully managed data processing jobs and data labeling workflows Web-based IDE for ML Automatically build and train models Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models AmazonSageMakerhelps you build,train,and deploy models
12. Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Prepare Build Train & Tune Deploy & Manage 101011010 010101010 000011110 Fully managed data processing jobs and data labeling workflows Classification • Linear Learner • XGBoost • KNN Working with text • BlazingText • Supervised • Unsupervised Computer vision • Image classification • Object detection • Semantic segmentation Recommendation • Factorization machines Anomaly detection • Random Cut Forests • IP insights Regression • Linear Learner • XGBoost • KNN Topic modeling • LDA • NTM Forecasting • DeepAR forecasting Feature reduction • PCA • Object2Vec AmazonSageMakerhelps you build,train,and deploy models One-click notebooks, built-in algorithms and models
13. Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 One-click training AmazonSageMakerhelps you build,train,and deploy models Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Web-based IDE for ML Automatically build and train models Fully managed data processing jobs and data labeling workflows One-click notebooks, built-in algorithms and models
14. Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 One-click training Debugging and optimization AmazonSageMakerhelps you build,train,and deploy models Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Web-based IDE for ML Automatically build and train models Fully managed data processing jobs and data labeling workflows One-click notebooks, built-in algorithms and models
15. Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 One-click training Debugging and optimization Visually track and compare experiments Web-based IDE for ML Automatically build and train models Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models AmazonSageMakerhelps you build,train,and deploy models Fully managed data processing jobs and data labeling workflows One-click notebooks, built-in algorithms and models
16. Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 One-click training Debugging and optimization Visually track and compare experiments One-click deployment and automatic scaling AmazonSageMakerhelps you build,train,and deploy models Web-based IDE for ML Automatically build and train models Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Fully managed data processing jobs and data labeling workflows One-click notebooks, built-in algorithms and models
17. Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 One-click training Debugging and optimization Visually track and compare experiments Automatically spot concept drift Web-based IDE for ML Automatically build and train models Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models AmazonSageMakerhelps you build,train,and deploy models One-click deployment and automatic scaling Fully managed data processing jobs and data labeling workflows One-click notebooks, built-in algorithms and models
18. Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 One-click training Debugging and optimization Visually track and compare experiments Add human review of predictions Web-based IDE for ML Automatically build and train models Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models AmazonSageMakerhelps you build,train,and deploy models One-click deployment and automatic scaling Fully managed data processing jobs and data labeling workflows One-click notebooks, built-in algorithms and models Automatically spot concept drift
19. Prepare Build Train & tune Deploy & manage 101011010 010101010 000011110 One-click training Debugging and optimization Visually track and compare experiments Add human review of predictions Fully managed with automatic scaling for 75% less Web-based IDE for ML Automatically build and train models Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models AmazonSageMakerhelps you build,train,and deploy models One-click deployment and automatic scaling Fully managed data processing jobs and data labeling workflows One-click notebooks, built-in algorithms and models Automatically spot concept drift
21. One interface to review past evaluations and detection logic Pre-built fraud detection model templates Models learn from past attempts to defraud Amazon Automatic creation of custom fraud detection models Amazon SageMaker integration New: Automate online fraud detection with Amazon Fraud Detector
22. Amazon Personalize • Fully managed service for generating personalized recommendations • Uses the same ML technology that is being used at Amazon.com • Generates highly relevant recommendations using deep learning techniques • Does not require ML expertise to use • Builds custom and private ML models using your own data Based on the technology that powers personalization at Amazon.com
23. Customized personalization API Amazon Personalize User events and interactions User metadata (demographics – optional) Item metadata (catalog information – optional) Fully managed by Amazon Personalize Amazon Personalize Behind the scenes Inspect data Select hyper- parameters Train models Optimize models Host models Real-time feature store Identify features
24. Key features Context-aware recommendations Automated ML Bring existing algorithms from Amazon SageMaker Continuous learning to improve performance Amazon Personalize Improve customer experiences with personalization and recommendations
26. Culture Setting your organization up for success
27. Enabling thenextML developers Partnerships with MOOCs ML educational devices AWS DeepLens Deep learning Training and certification AWS ML training and certification AWS DeepComposer Generative AI AWS DeepRacer Reinforcement learning
28. The world’s first machine learning-enabled musical keyboard for developers
29. Choose from jazz, rock, pop, or classical, or build your own custom genre model in Amazon SageMaker Input a melody by connecting the AWS DeepComposer keyboard Publish your tracks to SoundCloud from the console; export MIDI files to your favorite DAW Creativemeets generative
30. AWS MachineLearningSolutions Lab
31. powered by
32. LearnmachinelearningwithAWSTrainingandCertification Learn at your convenience with 65+ free digital courses, or register for a live instructor-led class featuring hands-on labs and opportunities for practical application Explore tailored machine learning (ML) paths for business decision makers, data platform engineers, data scientists, and developers Take the AWS Certified Machine Learning – Specialty exam to validate expertise in building, training, tuning, and deploying ML models Visit the ML learning paths at https://aws.training/ML Resources created by the experts at AWS to help you build and validate machine learning skills
33. Thank you! © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

Hinweis der Redaktion

First call deck for CXO level introduction to Amazon AI.
最近很多大規模的宣傳在 AI/ML 正對 digitial transformation

50年前已經有 ML technology

最近最大的改變是因為 Cloud Computing - ML technology 更容易使用和取得

IDC: by 2021, global spending on AI and cognitive technologies will exceed $50 billion.

在幾個 use case
Customer experience
conversational interface （對話方式, chatbot)
personalization and recommendation
Business operation and Decision Making
透過 Forecast - improve demand planning 的正確度
Innovation & Competitive Advantage
Supply chain management 的 automation
Healthcare - reactive to predictive care
Simplify Machine Learning
Easy for developer to build smart application
Broadest & Deepest set of AI/ML services
在去年 AWS launched 200+ new features & services
Developer 透過這些技術去開發簡單/複雜的 AI applications

透過 Amazon Sagemaker 加速 ML 的 adoption
Sagemaker: 讓你可以更簡單的 build, train 和 deploy
…

Most comprehensive (全方位）的 cloud platform
Not only ML, but also security, analytics, compute services, etc. should be put into consideration
AWS 有全方位的 technology
Gartner ..
10,000+ 客戶在 AWS 上面跑 ML - More than anywhere else
From startup to enterprise
Mention: Nascar, Intuit, NFL, etc.
Technology
正對 enterprise 利用 AI 這件事情 - 我們可以用2個面向:
Technology — 解決的問題不一樣，需要的工具耶不一樣
AWS ML Stack
AWS provide 提供完整的 ML 技術
不同的客戶角色 - 利用不同的 layer

ML fw & infrastructure
For expert ML practitioner
開發 algorithm ，maintain 自己的 infrastructure，等等
提供常用的: tensor flow, MXnet, pytorch
ML practitioner 通常會用多個 framework 解決問題
根據 Research
85% tensor flow / 83% pytorch running on AWS
Provide deep learning AMI and container
Quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks to train AI models
Amazon Elastic Inference
Attach low cost GPU 加速功能附加到 Amazon EC2 和 Sagemaker instances 或 Amazon ECS 任務，最多可節省 75% cost.
ML Services
Not many ML practitioner
Want to make it easy for data scientist build, train, and deploy
Amazon SageMaker
ML Workflow is complex
ML development
complex
no integrated tool for end to end flow
Prepare data —>
Build / need to write your own algorithm / choosing the right algorithm
Train & Tune
Prepare and manage training environment
Model tuning
Depoy & Manage
Host inference
How to scale production environment
How to monitor models
Etc.
Amazon Sagemaker help you on this
Let’s see how
Prepare
data collection and prep is important in ML
Build and Manage training data is often complicated and time-consuming
Amazon SageMaker Ground Truth helps you build and manage training datasets.
your own labelers / Amazon Mechanical Turk / Vendor from Marketplace
continuously learns from labels done by humans to make automatic annotations
Build
Amazon SageMaker Notebooks
Managed Jupyter Notebook
compute resources are fully elastic – scale up / down
one-click sharing of notebooks. All code dependencies are automatically captured, so you can easily collaborate with others.
Algorithm
pre-build algorithms
Choose from pre-built:
supervised algorithms such as XGBoost and linear/logistic regression or classification,
unsupervised learning such as with k-means clustering and principal component analysis (PCA)
You can also shop for algorithms, models, and data in the AWS Marketplace.
Bring your own algorithm - docker container
Train & Tune
One Click training
It’s easy to train model in SageMaker
Specify data location in Amazon S3
One click - SageMaker help you to setup and manage environment
compute cluster perform training
output to s3
Tear down cluster

optimized data streams from Amazon S3 to SageMaker.

can specify data distribution based on your algorithm: if you’d like all of the data to be sent to each node in the cluster, or Amazon SageMaker to manage the distribution of data
Debugging and Optimization

在開發過程中遇到 error 需要 debug / tune
Amazon SageMaker Debugger
Full visibility of ML training process
automatically detects and alerts developers to occurring errors
make it easy for developer to debug
complete insights 資訊 into the debugging process.
Compare Experiments
What if you have multiple model to experiment that you want to compare
Amazon SageMaker Experiments – track and compare models
Evaluate training experiement
record training artifacts including datasets, algorithms, parameters
system automatically organizes, ranks experiments based on chosen metric, and produces data visualizations
quickly compare and identify the best performing models
Deploy & Manage
Amazon SageMaker makes it easy to deploy your trained model into production with a single click
auto-scaling Amazon ML instances across multiple availability zones for high redundancy.
Amazon SageMaker will launch the instances, deploy your model, and set up the secure HTTPS endpoint for your application
Your application simply needs to include an API call for inference endpoint.
Automatically Spot concept drift
Have error in production?
Amazon SageMaker Model Monitor monitors models in production and detects errors so you can take remedial actions.
Analyzes the data collected at regular frequency based on built-in rules or customer-provided rules
Integrated with CW - metrics emitted in CloudWatch so you can set up alarms 做 notification
Add human review
Many machine learning applications require humans to review low confidence predictions to ensure the results are correct.
Amazon Augmented AI (Amazon A2I)
build the workflows required for human review of ML predictions.
Scalable prod environment
Auto scale ML instances - specify min and max

End to end — Web-based IDE for ML development: Amazon SageMaker Studio
Automatically build and train models based on your data - SageMaker Autopilot
allowing to maintain full control and visibility.
Process is completely transparent.
dive into the details of how it was created refine based on your needs
Now, at the top of the stack

For developers who want to add solution-oriented intelligence to their applications through an API call rather than developing and training their own models

Vision (Rek Image and video) “tell me what’s in this image,” “which celebrities are in this image,” and, “is this image safe for work?”

Speech – (Polly, Transcribe) – If you want to turn text into speech we have Polly, and if you want to transcribe audio we have Transcribe

Text (translate, comprehend, textract) – If you want to translate text to different languages we have translate, and if you want to understand what’s being said in those translated corpuses of text you have Comprehend, if you want to extract text from documents using OCR++, you have Textract.

Then you have the things which Amazon is relatively famous for. Alexa and chatbots with Lex, personalization, and forecasting

Customers have asked us to continue to consider adding services around areas where we have a lot of experience and data from our consumer business…

Amazon Fraud Detector, a new fraud management service for detecting online identify and payment fraud in real-time. No machine learning experience required.

Amazon CodeGuru, a new machine learning service to automate code reviews and identify your most expensive line of code.

Kendra, a new service which reinvents enterprise search with machine learning.

Contact Lens for Amazon Connect provides machine learning powered contact center analytics for Amazon Connect
Amazon Fraud Detector
Fraud teams often lack in-house ML experts
Amazon FraudDetector - 全受管詐騙偵測器服務
根據Amazon 超過 20 年的詐騙偵測專業知識
Use case:
- new account registration
- EC order checkout
- online payment
How?
- Upload your historical fraud datasets to Amazon S3
- Select from pre-built fraud detection model templates
- automatically train, test, and deploy a customized fraud detection model
your appliation can call the API to do inference
Amazon Personalize 就像您專屬的 Amazon.com 機器學習推薦團隊

Personalization project, typical>6mo, now you can get it done in weeks

The technology behind is well tested for performance and scale

Personalize uses deep learning sequence model which greatly improve result

Personalize can be used by all developers and does not need ML expertise

Models are private to you and only used for generating recommendations for your users
Start with providing 3 kinds of datasets

User interactions – what are the users clicking on, what are they purchasing, what are they watching. This has the strongest signal for personalization and is the only dataset compulsorily needed. The other two are optional
Inventory (aka catalog) – details about your items. Items IDs + price, category, genre (if books), type etc.
User details – details about the user – age, gender etc.

Once you get the data in it is very simple to get a trained personalization model in Personalize

Under the hood Personalize does feature engineering, section of hyperparameters, model training, hosting, deployment and management

Models are private to you and only used for generating recommendations for your users
With Amazon Personalize you are able to delight your customers.
Deliver high quality recommendations in real time

Train a recommendation model with a few clicks

Generate recommendations for almost any product or content
To summarize, the AWS AI and ML stack has three layers. Each layer addressing different audiences:
ML Frameworks & Infrastructure: For expert machine learning practitioners who work at the framework level and are comfortable building, training, tuning, and deploying machine learning models.
ML Services: For every day developers and data scientists we built and launched Amazon SageMaker, a managed ML service to build, train, and deploy machine learning models quickly.
AI Services: Developers with no prior machine learning experience can easily build sophisticated AI driven applications, like an AI driven contact center, live media subtitling, understanding voice of the customer, content moderation, or identity safety and verification, often
While AI technology is important to build successful AI initiatives, the organization culture is also important—

skills in-house or can partner for expertise
We have created a range of ways for developers of any skill levels to get started with machine learning.

2./ We’ve created a portfolio of educational devices to help put new machine learning techniques into the hands of developers in unique and fun ways, with AWS DeepLens, AWS DeepRacer, and the AWS DeepRacer League – and now AWS DeepComposer.

For more traditional classroom-based learning, there are a few options.
4./ We have a range of online and training available – most of which you can get started for free!
5./ AWS ML training, the curriculum that is used to train Amazon developers is available to you through AWS, for free
6./ We also partner closely with leading online course providers such as Udacity, Coursera and edX to bring relevant courses that help developers build their machine learning skills.
What is AWS DeepComposer?

AWS DeepComposer gives developers a creative way to get started building generative AI models – hands on with a musical keyboard -
create a melody that will transform into a completely original song in seconds, all powered by AI.

Generative AI is one of the biggest recent advancements in artificial intelligence technology because of its ability to create something new.
1/ input a melody by connecting the AWS DeepComposer keyboard to your computer, or play the virtual keyboard in the AWS DeepComposer console.
AWS DeepComposer accepts music in MIDI format.

2/ Choose from jazz, rock, pop, symphony or Jonathan Coulton pre-trained models.
you can also build your own custom genre model in Amazon SageMaker.

3/ You can then publish your tracks to SoundCloud in one click, or export MIDI files to your favorite Digital Audio Workstation (like Garage Band) and get even more creative.
Consultant Service

The Amazon ML Solutions Lab pairs your team with Amazon machine learning experts to prepare data, build and train models, and put models into production.

hands-on educational workshops with brainstorming sessions, ‘work backwards’ from business challenges, and then go step-by-step through the process of developing machine learning-based solutions.

At the end of the program, you will be able to take what you have learned through the process and use it elsewhere in your organization to apply ML to business opportunities.

We help customers adopt ML with more than 145 engagements so far including AstraZeneca, NFL, and the MLB.
NextGen stats

We helped bring together domain experts and technical experts in our ML Solutions Lab

Capture and process data – use EC2, S3, and EMR

Machine learning models built on Amazon SageMaker to output predictions, stats and more.

Distribute result - leverages AWS Lambda, Amazon ElastiCache, Quicksight, RDS, Route S3, API gateway, and DynamoDB to put the insights, predictions, and stats into broadcast analysis, scouting, and coaching tools.

台灣 customer – Formosa Plastic / 台塑
What is next?

machine learning curriculum we used to train thousands of Amazon’s own developers and made it available digitally, for free.

We also offer live classes with accredited AWS instructors who teach using a mix of presentations, discussion, and hands-on labs.

Explore tailored learning paths by role for ML at aws.training/ML

Track 6 Session 3_如何藉由 AWS AI 和機器學習平台搭建多功能的 AI 解決方案.pptx

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