인공지능클라우드로진화하는클라우드

2019. 12

한상기 / 테크프론티어 대표

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AI-First Cloud

Support for mainstream AI frameworks -- be able to run deep learning or AI applications implemented in mainstream frameworks such as TensorFlow, Caffe, Theano, Torch, etc.

GPU optimized infrastructure

Management tools

AI-first infrastructure services -- AI being a key element to improve the intelligence of cloud services such as storage, compute, or security

Integration with mainstream PaaS services

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초기에는 딥러닝 프레임워크 지원 3

Machine Learning as a Service (2017)

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누구나 AI-First

전략이필요

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“Gartner Says AI Technologies

Will Be in Almost Every New

Software Product by 2020”

By 2022, more than 80% of

enterprise IoT projects will have

an AI component, up from less

than 10% today

AI 도입의어려움 6

AIaaS (AI as a Service)7

AIaaS(Artificial Intelligence as a Service) incorporates a range of services that offer AI tools through cloud computing services. (Markets and Markets)

Google, AWS, IBM, Microsoft, and Apple Inc. are some of the leading players of the global AIaaS market

AIaaS can be defined as outsourcing of artificial intelligence enabled technology and solutions by an enterprise. (marketwatch.com)

AIaaS provides several benefits to an organization over traditional approach such as it provides advanced infrastructure at minimal cost, transparency in business operations, and scalability

AI Service – SaaS

ML Service – PaaS

AI Computing Resource - IaaS

AIaaS

Market

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2018년 15.2억 달러 (1.82조 원) 규모를 형성한 시장이연평균 48.2% 성장해 2023년에 108.8억 달러 (13.05조 원)

– Markets and Markets

Google Cloud AI - Overview 9

Google Cloud AI – ML Process

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AutoML Tables베타

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데이터과학자에서 분석가, 개발자에 이르기까지 전체팀에서 엄청난 속도와 확장성의 개선을 가져오는구조화된 데이터 기반 최신 머신 러닝 모델을 자동으로빌드하고 배포하도록 지원

Google What-IF

Tool (Fairness)

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PAIR The People + AI Research initiative (PAIR)

Interactive visual interface designed to probe your models better

Compatible with TensorBoard, Jupyterand Colaboratorynotebooks. Works on Tensorflow and Python-accessible models

Google

Explainable

AIBETA

13Tools and frameworks to deploy interpretable and inclusive machine learning models

Google

AI Hub

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Machine Learning on AWS

Amazon Pre-Trained AI Services

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Amazon SageMakerA fully managed service that provides every developer and data

scientist with the ability to build, train, and deploy machine

learning (ML) models quickly

Amazon

SageMakerAutopilotautomatically trains and tunes

the best machine learning

models for classification or

regression, based on your data

while allowing to maintain full

control and visibility

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Amazon SageMakerStudio

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Fully integrated

development environment (IDE) for

building and training

machine learning

workflows

Amazon Augmented AI (Amazon A2I) 20

Amazon A2I provides built-in human review workflows for common machine learning use cases, such as content moderation and text extraction from documents, which allows predictions from Amazon Rekognition and Amazon Textract to be reviewed easily

Microsoft Azure AI

Azure AI - a set of AI services built on Microsoft’s breakthrough

innovation from decades of world-class research in vision, speech, language processing, and custom machine learning

Develop machine learning models that can help with scenarios such as

demand forecasting, recommendations, or fraud detection using Azure

Machine Learning.

Incorporate vision, speech, and language understanding capabilities

into AI applications and bots, with Azure Cognitive Services and Azure

Bot Service.

Build knowledge-mining solutions to make better use of untapped

information in their content and documents using Azure Search

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Microsoft Azure Cognitive Service

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Cognitive Services are for developers without machine-learning experience

Provides a trained model for you

Azure Machine Learning23

BOOST PRODUCTIVITY AND ACCESS ML FOR ALL SKILLS - ACCELERATE MODEL

CREATION WITH THE AUTOMATED MACHINE LEARNING UI, AND ACCESS

BUILT-IN FEATURE ENGINEERING, ALGORITHM SELECTION, AND

HYPERPARAMETER SWEEPING TO DEVELOP HIGHLY ACCURATE MODELS

OPERATIONALIZE AT SCALE WITH ROBUST MLOPS - MLOPS,

OR DEVOPS FOR MACHINE LEARNING, STREAMLINES THE

MACHINE LEARNING LIFECYCLE, FROM BUILDING

MODELS TO DEPLOYMENT AND MANAGEMENT.

BUILD RESPONSIBLE AI SOLUTIONS – FAIRNESS AND

TRANSPARENCY

INNOVATE ON AN OPEN AND FLEXIBLE PLATFORM – OPEN

SOURCE TOOLS AND FRAMEWORKS

Azure Machine Learning is tailored for data scientists

New Features at Ignite 2019

New studio web experience

New industry-leading Machine Learning Operations (MLOps)

Provide choice and flexibility with support for R, Azure Synapse

Analytics, Azure Open Datasets, ONNX, and other popular

frameworks, languages, and tools

New security and governance features including role-based access

control (RBAC), Azure Virtual Network (VNet), capacity

management, and state-of-the-art responsible AI interpretability

and fairness capabilities

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Azure Machine Learning—ML for all skill levels

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Flexible authoring options

from no-code drag-and-drop and automated machine

learning, to code-first

development

Azure ML - Open and Flexible

Platform

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NBP

Cloud AI

Service

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Clova Platform – NAVER and Line

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Saltlux Cloud Service

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AI Cloud(ADAMS.ai) 서비스는 솔트룩스가 지난20년간 자연언어처리와 시맨틱, 추론을 포함한인공지능 원천기술을 바탕으로 개발된언어지능, 시각지능, 감성지능, 학습/추론지능을 인공지능 서비스 개발에 필요한 API 형태로 제공하는 클라우드서비스

MIND’S LAB maum.ai

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AI and Edge

Cloud

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Microsoft Intelligent Edge

Amazon SageMaker Neo AI and Greengrass

Google Cloud IoT Edge

마치면서

국내 클라우드 컴퓨팅 환경도 빠르게 AIaaS에 대응해야 하며

데이터 준비부터 모델 개발과 선정, 검증, 비교 분석까지 매우종합적인 서비스 제공이 필요

국내 데이터 특성과 고객 수준에 맞게 차별화 할 수 있고

Trustworthy AI를 지향하기 위한 투명서/공정성/윤리/책무성/견고성과안전성 등에서 도메인 또는 국내 환경에 맞는 도구의개발이 필요

오픈과 협업의 방식이 절대적으로 필요

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AI Hub to Provide AI Data, AI Software, and AI Computing

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더 많은 구독과 활용 바랍니다

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감사합니다

(Meet me at facebook.com/stevehan

또는 ‘책과얽힘’)

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