APIs的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列地圖、推薦、景點和餐廳等資訊懶人包

Programming Google Cloud: Building Cloud Native Applications with Gcp

為了解決APIs的問題，作者Costa, Rui 這樣論述：

Companies looking to move enterprise applications to the cloud are busy weighing several options, such as the use of containers, machine learning, and serverless computing. There’s a better way. Instead of helping you fit your use case to individual technologies, this practical guide explains how

to use these technologies to fit your use case. Author Rui Costa, a learning consultant with Google, demonstrates this approach by showing you how to run your application on Google Cloud. Each chapter is dedicated to an area of technology that you need to address when planning and deploying your a

pplication. This book starts by presenting a detailed fictional use case, followed by chapters that focus on the building blocks necessary to deploy a secure enterprise application successfully. Build serverless applications with Google Cloud Functions Explore use cases for deploying a real-time mes

saging service Deploy applications to Google Kubernetes Engine (GKE) Build multiregional GKE clusters Integrate continuous integration and continuous delivery with your application Incorporate Google Cloud APIs, including speech-to-text and data loss prevention Enrich data with Google Cloud Dataflow

Secure your application with Google Cloud Identity-Aware Proxy Explore BigQuery and visualization with Looker and BigQuery SDKs

APIs進入發燒排行的影片

台北車站室內導航尋路策略及介面型式之研究

為了解決APIs的問題，作者張晶雅 這樣論述：

尋路是人一項重要的能力，當我們在尋路的過程中遇到困難時，容易產生負面的生理與心理感受，室內尋路行為涉及水平與垂直的移動，相較室外尋路更加複雜。隨著科技發展，已有許多輔助尋路的導航工具運用於室內空間，而目前數位室內導航工具的介面型式主要以圖像式介面為主，然而隨著對話式介面的應用逐漸提升，卻較少見對話式介面型式之導航工具，因此本研究將探討尋路行為與介面型式應用於導航之影響，期望能透過本研究探討導航輔助工具更多的可能性。本研究實驗分為兩階段：(1) 前導實驗邀請四位受測者以口語描述路徑的方式，定義出指定路線中之關鍵地標，將實驗結果彙整出指定路線之路徑描述，並且建置出指定路線的導航內容，將其應用於第

二階段的驗證實驗 (2)第二階段的驗證實驗以3 (俯瞰式、路徑式、混合式) × 2 (介面式、路徑式) 的雙因子組間的實驗設計方式進行，邀請共 48 位的受測者以線上測試的型式，個別操作 6 項實驗樣本進行測試，並測量依變數：判斷方向錯誤次數、任務時間績效、系統易用性尺度量表 (SUS)、 NASA-工作負荷量表 (NASA-TLX)、地標記憶數量。實驗結果得知使用圖像式介面與對話式介面之導航工具對尋路行為皆無顯著差異，而採用不同尋路策略之尋路工具在跨樓層之室內環境會影響尋路行為，介面型式會對受測者採用尋路策略產生影響，若對話式介面型式之導航工具能具有更佳的介面設計彈性，則將能有助於提升導航輔

助工具應用於社群軟體上之聊天機器人的尋路表現。本研究提出之設計建議如下：(1) 路徑式尋路策略較適用於跨樓層的室內導航；(2) 對話式介面受限於平台的規格，其資訊呈現方式的彈性較小； (3) 樓層轉換可視為一個新的路徑起點，應在樓層起點以地標輔助重新定位；(4) 室內導航應提供足夠且精簡的導航資訊，而非越多越好。

Microprediction: Building an Open AI Network

為了解決APIs的問題，作者Cotton, Peter 這樣論述：

How a web-scale network of autonomous micromanagers can challenge the AI revolution and combat the high cost of quantitative business optimization.The artificial intelligence (AI) revolution is leaving behind small businesses and organizations that cannot afford in-house teams of data scientists.

In Microprediction, Peter Cotton examines the repeated quantitative tasks that drive business optimization from the perspectives of economics, statistics, decision making under uncertainty, and privacy concerns. He asks what things currently described as AI are not ＂microprediction,＂ whether microp

rediction is an individual or collective activity, and how we can produce and distribute high-quality microprediction at low cost. The world is missing a public utility, he concludes, while companies are missing an important strategic approach that would enable them to benefit--and also give back. I

n an engaging, colloquial style, Cotton argues that market-inspired ＂superminds＂ are likely to be very effective compared with other orchestration mechanisms in the domain of microprediction. He presents an ambitious yet practical alternative to the expensive ＂artisan＂ data science that currently dr

ains money from firms. Challenging the machine learning revolution and exposing a contradiction at its heart, he offers engineers a new liberty: no longer reliant on quantitative experts, they are free to create intelligent applications using general-purpose application programming interfaces (APIs)

and libraries. He describes work underway to encourage this approach, one that he says might someday prove to be as valuable to businesses--and society at large--as the internet.

用於整合開放基站開源軟體社群分散單元的支援工具

為了解決APIs的問題，作者Jonathan 這樣論述：

O-RAN 軟體社群 (O-RAN Software Community, OSC)致力於開發建構開放式無線接取網路(Open RAN, O-RAN)的開源軟體模組。OSC 涵蓋的範圍包括文件、測試、整合和其他軟體模組的部署，以利於開源軟體項目的開發、部署、操作或使用。在本論文中，我們開發了三個工具來支援 OSC 計畫的文件化、開發和部署。我們使用O-RAN 分佈式單元 (O-DU Low 和 O-DU High) 計畫為例，來展示如何使用這些工具來簡化相關工作。我們開發的文件化工具，可以將廠商現有以Word格式紀錄的技術文件，根據OSC社群的規範，轉換成reStructuredText (

RST) 格式。我們在O-DU Low計畫中，使用這個工具來協助英特爾來轉化技術文件。此外，我們也提出一個可以自動產生訊息序列圖(Message Sequence Chart, MSC)的開發工具，它可以將紀錄網路流量的封包捕獲 (Packet Capture, PCAP) 檔案，在 HackMD(一個群體協作軟體)平台上，自動畫出訊息序列圖。我們使用該工具分別分析驗證了O-DU High 計畫的 F1 和 E2 介面，以及O-DU Low 計畫的開放式前傳介面(Open Fronthaul Interface, FHI)的訊息交換流程。最後，我們還提出了一個部署工具，它使用系統腳本方法來完成

伺服器的自動化配置，我們O-DU High模組為例，展示如何使用此工具來減少手動部署的工作量和整合時間。