Building AI Platform on Azure

Fred Chong

Principle Architect

James Ren

Senior Data Scientist

张京雷Airdoc 副总裁

AI 301

Microsoft Tech Summit 2017

微软技术暨生态大会

Overview

• Microsoft AI Platform

• Image Classification Walkthrough

• Customer Case Study - Airdoc

Filtering the signal from the noiseUsed analytics to discover actionable insights around

fuel usage, predictive maintenance and stop

unscheduled delays.

Click

IoT-enabled Smart FridgeWeka builds a life-saving smart fridge using Azure IoT

Suite and Windows 10 IoT

Transforming the urban landscapeGathered data from sensors and systems to create

valuable business intelligence and shift from reactive to

proactive maintenance.

Click

Building a connected hospitalReduce medication delivery times and inventory costs by

connecting dispensary devices with electronic medical

records.

Empowering global operationsCollected data from 40,000 sensors around the world

to create a best-in-class forecast that helps customers

make critical operational decisions.

Building efficiency with IoTConnected chillers are back online 9x faster than

unconnected equipment, avoiding more than $300,000

in hourly downtime costs

Centralized business intelligence Centralized access to analytics, project data, and

collaboration tools by more than 12,000 employees.

Barcelona realizes vision of

innovative city Saved 30 percent of the cost of an on-premises

solution by migrating to the cloud.

Systems of Intelligence

Transform products

Engage customers

Optimize operations

Empower employees

Systems of Intelligence are hard to build and operate

• E2E integration of infrastructure, data, AI, actions, feedback

• Cut across boundaries of mission critical production, data warehousing

and AI experimentation environments

Customers want:

• A strategic partner for their Systems of Intelligence.

• Well-engineered platforms, not one-off consultant built solutions.

Key Customer Learnings

The Microsoft AI platform: Azure+AICloud-powered AI for every developer

Services

Infrastructure

Tools

Productive, Scale-out, E2E Full-lifecycle AI Development and Operationalization

Model ManagementDeploy, Version, Manage &

Monitor Models

WorkbenchWrangle Data, Build

models, Deploy & Manage

ExperimentationBoost productivity with agile

development.

Program Synthesis SPARK, GPU, Open Source

Lifecycle Management

Docker, Spark, IOT Edge, On prem, AWS/GCP…

DevOps and Delivering AI Solutions

https://www.microsoft.com/en-us/cloud-platform/development-operations

http://www.itproguy.com/devops-practices/

Configure Code Build Test Package Deploy

Monitor

AI / ML / DL

Adoption of DevOps practices key for

operationalizing AI

• Infrastructure as Code (IaC)• Automated Testing• Continuous Integration• Continuous Deployment• Release Management• App Performance Monitoring

Team Data Science Project (TDSP)

Standardized Data Science Lifecycle

Project Structure, Templates & Roles

Infrastructure

Re-usable Data Science Utilities

Using TDSP within Azure Machine Learning

Data Science VM

• Custom VM image on Azure Marketplace

• Contains a set of data science, Azure/tools/SDKs

• All pre-configured and ready to use

• Pay for cloud hardware usage only, no separate software charges

• Pointers to gallery, samples, documentations

• Windows and Linux versions

图像分类演示

• 数据处理• 用迁移学习进行模型训练和测试• 用Azure CLI 部署深度学习模型• 所有工作均在AML Workbench完成• 数据集:

车辆前盖或车体照片

车辆应用场景

保险行业

质量控制

车辆应用场景

图片

质量控制

利用迁移学习微调

利用预先训练好的深度学习模型，不需要百万张图片，

只需要几百张甚至更少的图片就可以训练出精准的模型

利用迁移学习微调

性能比较

自建深度学习模型 迁移学习微调

DEMO

https://github.com/Azure/China-Data-Solutions/tree/master/AI/ImageClassification

主要结论

DSVM为数据科学家提供全套机器学习工具

迁移学习让深度学习变得更简单，更快捷

AML Workbench 提供从模型训练到部署的端到端解决方案

2017微软技术暨生态大会张京雷，Airdoc 副总裁2017年11月3日，北京

Azure辅助医学人工智能赋能基层卫生服务

端到端用户实例 — Airdoc

vi

从Alpha Go到医疗AI

训练数据 模型参数 Error

Airdoc与医疗AI

Our Mission is to Make Healthcare Easy via AI

我们的使命是通过人工智能让医疗更高效！

• 中国人工智能学会智慧医疗专委会秘书长单位

• 国家卫计委基层医联网首批合作伙伴

• 新智元2016中国最具影响力及潜力人工智能企业

• 科技部STI智能医学影像中心发起单位 / STI眼科大数据联合实验室人工智能示范基地

Airdoc走出国门

Airdoc作为唯一一家中国人工智能企业，受邀参加了微软Build2017全球开发者

大会，是微软从140多个国家筛选出的最佳医疗案例。

会议开幕式环节，美国电气电子工程协会院士及国际计算机协会院士，微软全球执行副总裁沈向洋博士在会上展示Airdoc与上海长征医院的糖尿病视网膜病变人工智能识别应用案例。

医学人工智能赋能基层卫生服务案例：深度学习算法及云平台支持眼科慢病筛查

CloudGateway

non-fundus-filter

QualityControl

Preprocess&

Normalization

General Classification

DetectionSegmentatio

n

Post-process

Decision Maker & Drawer

Azure Cloud

Data Base

Client

webserver

科技部 database

深度学习（微软CNTK框架）识别眼底病变

18 万张眼底照片

多名专家交叉标注

100+层卷积神经网络

97%准确度

+

More Intelligent, Better Care

➢ 糖尿病自动识别和标注以下病变： ➢ 算法性能：

算法AUC曲线

✓ 最优敏感度点敏感度：98.3%特异性：92.2%

✓ 最优特异性点敏感度：92.7% 特异性：98.1%

✓ 最优平衡点敏感度：96.8%特异性：97.3%

识别一张DR照片算法耗时80ms

眼底视网膜病变的深度学习

远程眼病识别工作流程

云端存储与服务使用Azure平台

Azure云平台支持的综合性解决方案

算法准确度高

Airdoc利用深度学习，构建Airdoc眼底分析算法，准确度高，除了正常疾病分级外，还能标注病灶部位。

适配各种型号眼底照相机

独立研发适配软件，无缝对接各类眼底照相机，将使用AI算法的难度降至最低，几乎无额外操作即可享有AI 算法服务。

阅片产品简单易用、功能强大

基于SaaS的服务模式，在云端管理患者信息，便于实时管理管控，了解筛查进展，生成统计信息。

AI 魔盒安全便捷

Airdoc知识产权的硬件盒子，应用于没有上网条件或上网受限环境，使用USB连接通讯，做到物理网络隔离。

大规模院外慢病筛查：从不可能到可能

强大的云平台Azure是大规模慢病筛查成为可能的幕后英雄

医联体参与慢病筛查：支持多级阅片审核强大的云平台Azure是各级、各科医生合作好助手

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