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進擊Ml ops 台灣產業ai的點線面_20210629
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AIOps 和 MLOps 都能幫助團隊實現落地:AIOps 自動化機器環境,而 MLOps 標準化流程。如果您負責AI團隊帶領,您應該要能組織兩者分工,不要將它們混淆為同一件事。
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進擊Ml ops 台灣產業ai的點線面_20210629
1.
進擊MLOps: 台灣產業AI的點線面 蔡明順 台灣人工智慧學校 2021/6/29
2.
Copyright© 台灣人工智慧學校 版權所有並保留一切權利 關於我 蔡明順
Richie Tsai 現職:台灣人工智慧學校 校務長 台大創創中心加速器 導師 行政院科技會報專家顧問 開放資料 (OpenData) 聯盟執行委員 專長:AI, Big Data, 數位轉型, SaaS 經歷:20多年經驗在資訊工程、產品管理、營運、 市場行銷、生態系統培育 • SAP 德商思愛普 大中華區資深副總經理 • Oracle 美商甲骨文 應用軟體部副總經理 • Teradata 美商天睿資訊系統 資深協理 Connect with 8000+ AI Engineers/ Data Scientists 2
3.
Copyright© 台灣人工智慧學校 版權所有並保留一切權利
3
4.
Copyright© 台灣人工智慧學校 版權所有並保留一切權利
4 人工智慧ETF已經噴發,未來還有極大的潛力
5.
Copyright© 台灣人工智慧學校 版權所有並保留一切權利 企業
AI 發展的關鍵重點 領域知識 + 數位科技技術 5
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6 Copyright© 台灣人工智慧學校 版權所有並保留一切權利 台灣產業AI的現在進行式 LEVEL
0 LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 4 LEVEL 5 LEVEL 6 AWARE INFRA DEFINE R&D OPERATE INFUSE DEMOCRATIZE 考慮導入AI中 企業準備導入 AI 。 並開始驅使企業朝著同 一個AI願景發展。 建立數據 INFRASTRUCTURE 無數據不AI,開始建立數據基礎架構 以確保獲得達成AI 願景目標所需的 高質量數據。 建立BUSINESS CASE與實驗 識別應用場景和建立商業 案例。開始檢驗數據,以 建立實際案例的PoC。 模型研發 確定部署預算來做AI,選 定工具、解決方案和伙伴, 以補強組織的執行能力。 項目與模型部署 AI 項目已經驗證,有部 署,已經體現商業價值。 擴大覆蓋範圍與部門 AI 正在被部署到更廣泛的業務。 投入更多的開發,更多的部門部 署,更多的團隊參與。 AI無處不在 AI 成為企業的DNA。 AI 將不再是一個獨立的計劃, 而是存在各個部門之間的 共同語言和合作。 THE ENTERPRISE AI MATURITY CYCLE 企業AI成熟曲線 (Source: 《2021 台灣企業AI 趨勢報告, State of Taiwan Enterprise AI Report》)
7.
7 Copyright© 台灣人工智慧學校 版權所有並保留一切權利 (Source:
《2021 台灣企業AI 趨勢報告, State of Taiwan Enterprise AI Report》) Future 展望未來 快速擴散的平台與應用將 崛起 •加快實驗/試錯的平台 (ie. AutoML) •自動化標準流程的平台 (ie. AIOps) •加深跨部門/團隊協作的平台 (ie. MLOps) •保持靈活性的數據平台 (ie. 數據虛擬化) •簡化AI使用的應用 (ie. Low Code/No Code)
8.
Photo credit: Pieter
Abbeel, Sergey Karayev, Josh Tobin
9.
Copyright© 台灣人工智慧學校 版權所有並保留一切權利
9 AI的團隊組成 A common data platform and workflow is crucial for enterprise success. Data Engineer ML Engineer Biz Analyst DevOps DevOps + ML Engineer App Developer (Credit: IBM Systems Lab Services) (all under the supervision of Data Scientist)
10.
Copyright© 台灣人工智慧學校 版權所有並保留一切權利
10 MLOps
11.
Copyright© 台灣人工智慧學校 版權所有並保留一切權利 AIOps
v.s MLOps AIOps :使用大數據、高級分析和機器學習技術來改進他們的 IT 基礎設施。 幫助 DevOps 和數據運營 (DataOps) 團隊選擇從開發到生產的自動化 內容,該學科幫助開源團隊預測性能問題、進行歸因分析、發現異常等 MLOps :提供數據科學和數據工程團隊協作平台。將機器學習演算法作為 產品持續管理,每個產品都有自己的連續生命週期。MLOps能助團隊從 工具、技術選擇、文件,一路構建、擴展和部署演算法到生產環境。 AIOps 和 MLOps 都能幫助團隊實現落地:AIOps 自動化機器環境,而 MLOps 標準化流程。如果您負責AI團隊帶領,您應該要能組織兩者分 工,不要將它們混淆為同一件事。 11
12.
Copyright© 台灣人工智慧學校 版權所有並保留一切權利 2.5% Innovators 創新者 13.5% Early
Adapters 早期採用者 34% Early Majority 早期大眾 34% Later Majority 晚期大眾 16% Laggards 落後者 Blooming means: 1. 領導企業展示許多成功效益 2. 產業AI化有許多標準產品與服務 3. 導入AI是必要投資且有專責部門 You are riding the crest of a wave 站在浪頭上 往前看, AI 技術採用週期 Blooming in coming Ch as m 2012 -2021Years 20 Years Blooming We are here Y2021- 2023 10 Years Mature Technological Singularity 2030 Slide credit : Taiwan AI Academy 12
13.
13 AIA Story
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Copyright© 台灣人工智慧學校 版權所有並保留一切權利 AIA
Story: 技術領袖培訓班 14
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Copyright© 台灣人工智慧學校 版權所有並保留一切權利
15 AIA Successful Story
16.
GPU x 512 AIA
Invested in HPC
17.
經理人班 技術領袖班 智慧醫療專班 Copyright© 台灣人工智慧學校 版權所有並保留一切權利
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Copyright© 台灣人工智慧學校 版權所有並保留一切權利
18 2020 Taiwan AI Landscape
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