AI：Algorithmia《2021 enterprise trends in machine learning 2021年机器学习的企业趋势》翻译与解读
AI：Algorithmia《2021 enterprise trends in machine learning 2021年機器學習的企業趨勢》翻譯與解讀
目錄
《2021 enterprise trends in machine learning 2021年機器學習的企業趨勢》翻譯與解讀
Methodology
Report themes
10 trends
2021: The year of ML
Priority shifts優先級轉變
Trend 1: AI/ML priority and budgets are increasing趨勢 1：AI/ML 優先級和預算正在增加
Trend 2: Customer experience, automation take priority趨勢二：客戶體驗、自動化優先
Trend 3: AI/ML “have” and “have-not” gap趨勢三：AI/ML“有”和“無”的差距
Challenges remain挑戰依然存在
Trend 4: Governance is top challenge by far趨勢 4：治理是迄今為止最大的挑戰
Trend 6: Need for organizational alignment趨勢 6：需要組織協調
Trend 7: Maturity limited by alignment issues趨勢 7：成熟度受限于對齊問題
The bottom line底線
Technical debt is piling up技術債務堆積如山
Trend 8: Deployment time is increasing趨勢 8：部署時間正在增加
Trend 9: Data scientists spend too much time on deployment趨勢 9：數據科學家在部署上花費過多時間
MLOps preferences偏好
Trend 10: Improved outcomes with MLOps solutions趨勢 10：使用 MLOps 解決方案改善結果
Conclusion
相關文章
AI：Algorithmia《2020 state of enterprise machine learning—2020年企業機器學習狀況》翻譯與解讀
AI：Algorithmia《2021 enterprise trends in machine learning 2021年機器學習的企業趨勢》翻譯與解讀
《2021 enterprise trends in machine learning 2021年機器學習的企業趨勢》翻譯與解讀
文章鏈接：https://info.algorithmia.com/2021-enterprise-trends-webinar-recording?utm_medium=website&utm_source=resources-page&utm_campaign=IC-2012-2021-ML-Trends
作者：Diego Oppenheimer，首席執行官
平臺：algorithmia.com | @algorithmia
簡介：迭戈·奧本海默(Diego Oppenheimer)是Algorithmia的聯合創始人兼首席執行官。此前，他設計、管理和發布了一些微軟最常用的數據分析產品，包括Excel、Power Pivot、SQL Server和Power BI。他持有卡內基梅隆大學(Carnegie Mellon University)的信息系統學士學位和商業智能和數據分析碩士學位。
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Methodology
| Random sampling of 403 respondents Business leaders involved in machine learning initiatives at their organizations Organizations with $100M or more in revenue Independent third-party | 隨機抽樣 403 名受訪者 在其組織中參與機器學習計劃的商業領袖 收入超過 1 億美元的組織 獨立第三方 |
Report themes
| Priority shifts Organizations are responding to economic uncertainty by dramatically increasing AI/ML investment Challenges remain Enterprises continue to face basic challenges across the ML lifecycle Technical debt is piling up Despite the increased investment, organizations are spending even more time and resources on model deployment MLOps preferences Organizations report improved outcomes with third-party MLOps solutions | 優先級轉變 企業組織正在通過大幅增加 AI/ML 投資來應對經濟不確定性 挑戰依然存在 企業在整個機器學習生命周期中繼續面臨基本挑戰 技術債務堆積如山 盡管增加了投資，但組織在模型部署上花費了更多的時間和資源 MLOps偏好 組織報告使用第三方 MLOps 解決方案改善了結果 |
10 trends
| Priority shifts 1. AI/ML priority and budgets are increasing 2. Customer experience and automation are top-priority use cases for AI/ML 3. There’s a gap between AI/ML “haves” and “have-nots” | 優先級轉變 1. AI/ML 優先級和預算正在增加 2. 客戶體驗和自動化是 AI/ML 的首要用例 3. AI/ML“有”和“無”之間存在差距 |
| Challenges remain 4. Governance is the top challenge by far 5. Basic integration and compatibility issues remain 6. AI/ML success requires organizational alignment 7. Companies lack organizational alignment maturity | 挑戰依然存在 4. 治理是迄今為止最大的挑戰 5. 基本的集成和兼容性問題仍然存在 6. AI/ML 的成功需要組織協調 7. 公司缺乏組織一致性成熟度 |
| Technical debt is piling up 8. Deployment time is increasing 9. Data scientists spend too much time on deployment | 技術債務堆積如山 8. 部署時間越來越長 9. 數據科學家在部署上花費太多時間 |
| MLOps preferences 10. Organizations report improved outcomes with MLOps solutions | MLOps偏好 10. 組織報告使用 MLOps 解決方案改善了結果 |
2021: The year of ML
| This was a year of increased urgency for ML—but initial efforts to scale only created more technical debt. In 2021, organizations that invest in MLOps will reap the greatest rewards. | 今年是機器學習緊迫性增加的一年——但最初的擴展努力只會產生更多的技術債務。 2021 年，投資于 MLOps 的組織將獲得最大的回報。 |
Priority shifts優先級轉變
Trend 1: AI/ML priority and budgets are increasing趨勢 1：AI/ML 優先級和預算正在增加
83% of organizations have increased A/ML budgets year-on-year
83% 的組織增加了 A/ML 預算
The average number of data scientists employed has increased76% year-on-year
數據科學家的平均就業人數同比增長了76%
Trend 2: Customer experience, automation take priority趨勢二：客戶體驗、自動化優先
The percentage of respondents who selected more than five use cases for AI/ML in our survey increased 74% year-on-year
Customer experience and process automation represent the top?AI/ML use cases
在我們的調查中，選擇超過 5 個 AI/ML 用例的受訪者比例同比增長 74%
客戶體驗和流程自動化代表了頂級 AI/ML 用例
For nearly all use cases,50% or more of organizations are increasing their usage of AI/ML
對于幾乎所有用例，50% 或更多的組織正在增加對 AI/ML 的使用
Trend 3: AI/ML “have” and “have-not” gap趨勢三：AI/ML“有”和“無”的差距
More than half of all respondents have more than 25 models in production.
40% of all respondents have more than 50 models in production.
超過一半的受訪者擁有超過 25 款型號在生產中。
40% 的受訪者擁有 50 多款型號在生產中。
The world's largest enterprises are dominating the high end of model scale
世界上最大的企業正在主導模型規模的高端
Challenges remain挑戰依然存在
Trend 4: Governance is top challenge by far趨勢 4：治理是迄今為止最大的挑戰
| 56% of organizations struggle with governance, security, andauditability issues | 56% 的組織在治理、安全和可審計性問題上苦苦掙扎 |
| When asked about regulations they need to comply with for AI/ML, 67% of respondents selected multiple regulations. Only 8% selected no regulations at all. 67% of all organizations must comply with multiple regulations. | 當被問及他們需要遵守的 AI/ML 法規時，67% 的受訪者選擇了多項法規。 只有 8% 的人根本沒有選擇任何法規。 67% 的組織必須遵守多項法規。 |
Trend 6: Need for organizational alignment趨勢 6：需要組織協調
Successful AI/ML initiatives involve decision-makers from across the organization
成功的 AI/ML 計劃涉及整個組織的決策者
Trend 7: Maturity limited by alignment issues趨勢 7：成熟度受限于對齊問題
Organizational alignment is the biggest gap in achieving AI/ML maturity
組織一致性是實現 AI/ML 成熟度的最大差距
The bottom line底線
| Stuck in the lab Disconnected teams Technology mismatch Stakeholder buy-in Hidden technical debt Inefficient machine learning lifecycle | 困在實驗室 斷開連接的團隊 技術不匹配 利益相關者的支持 隱藏的技術債務 低效的機器學習生命周期 |
| Stuck in the lab: Compliance with existing IT governance, security, and auditability requirements delays or prevents deployment. Disconnected teams: Difficulty aligning data science development needs with IT requirements for production. Technology mismatch: Missed opportunities to deploy models in time to capitalize on market opportunities. Stakeholder buy-in: Difficulty tracking ML investment outcomes for value delivered. Hidden technical debt:?Frequent updates, significant production testing, and constant validation required to maintain model quality?and performance. | 困在實驗室：遵守現有的 IT 治理、安全性和可審計性要求會延遲或阻止部署。 斷開連接的團隊：難以將數據科學開發需求與 IT 產品需求保持一致。 技術不匹配：錯過了及時部署模型以利用市場機會的機會。 利益相關者的支持：難以跟蹤 ML 投資結果以實現價值交付。 隱藏的技術債務：維護模型質量和性能所需的頻繁更新、重要的產品測試和持續驗證。 |
Technical debt is piling up技術債務堆積如山
Trend 8: Deployment time is increasing趨勢 8：部署時間正在增加
Only 11% of organizations can put a model into production within a week,and 64% take a month or longer
只有 11% 的組織可以在一周內將模型投入生產，而 64% 的組織需要一個月或更長時間
?The time required to deploy a model is increasing year-on-year
部署模型所需時間逐年增加
Trend 9: Data scientists spend too much time on deployment趨勢 9：數據科學家在部署上花費過多時間
38% of organizations spend more than 50% of their data scientists' time on deployment
38% 的企業將超過 50% 的數據科學家時間用于部署
Organizations with more models spend more of their data scientists' timeon deployment, not less
擁有更多模型的企業將更多的數據科學家的時間花在部署上，而不是更少
MLOps preferences偏好
Trend 10: Improved outcomes with MLOps solutions趨勢 10：使用 MLOps 解決方案改善結果
71% of all organizations have hybrid environments, and 42% have acombination of cloud and on-premises infrastructure
71% 的組織擁有混合環境，42% 擁有云和本地基礎架構的組合
42% of respondents have hybrid environments consisting of both cloud and on-premises solutions.
Last year, only 16% did.
42% 的受訪者擁有由云和本地解決方案組成的混合環境。
去年，只有 16% 的人這樣做了。
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| Buying a third-party solution costs 19-21% less than building your own | 購買第三方解決方案的成本比構建自己的解決方案低 19-21% |
| Respondents were asked to indicate theiraverage annual infrastructure costs based onpredefined ranges, such as "$51-$100K". Thetotal average annual infrastructure cost wasthen estimated as a range.The low estimate isbased on the lower bound for each predefinedrange (for example, $51K for “$51-$100K").The high estimate is based on the upper boundfor each predefined range (for example, $100Kfor"$51-$100K"). For the pre-defined rangethat represented the greatest cost ("more than$10M"), the lower bound of the range was usedfor both the high and low estimate.The percentdifference was calculated with the underlyingdata before rounding to the nearest?percentage point. | 受訪者被要求根據預定義的范圍（例如“$51-$100K”）說明他們的平均年度基礎設施成本。 然后將年平均基礎設施總成本估計為一個范圍。低估計基于每個預定義范圍的下限（例如，$51K 對應“$51-$100K”）。高估計基于每個預定義范圍的上限（ 例如，$100K 代表“$51-$100K”）。對于代表最大成本（“超過 1000 萬美元”）的預定義范圍，該范圍的下限用于最高和最低估計。百分比差異為 在四舍五入到最接近的百分點之前用基礎數據計算。 |
Organizations that buy a third-party solution spend less of theirdata scientists' time on model deployment
購買第三方解決方案的組織在模型部署上花費的數據科學家時間更少
The time required to deploy a model is 31% lower for organizations thatbuy a third-party solution
對于購買第三方解決方案的組織來說，部署一個模型所需的時間降低了31%
Conclusion
| 2021: The year of ML Next year will be a crucial year for AI/ML initiatives.There’s increased urgency—don’t get left behind. More accessible than ever Despite the increasing complexity of the space, it’s never been easier to start investing in AI/ML and scale it more effectively. You need MLOps Organizations that invest in operational efficiency will reap the greatest benefits in 2021. The time to act is now! | 2021：機器學習之年 明年將是 AI/ML 計劃的關鍵一年。緊迫性越來越高——不要落后。 比以往任何時候都更容易訪問 盡管該領域的復雜性越來越高，但開始投資 AI/ML 并更有效地擴展它從未如此簡單。 你需要 MLOps 投資于運營效率的組織將在 2021 年獲得最大的收益。現在是行動的時候了！ |
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