Tag: step

Feed: Microsoft Dynamics 365 Blog. Author: Manoj Swaminathan. Disruptions in global supply chains have significantly increased over the last decade, fueled by a myriad of triggers ranging from trade wars, demand surges from social marketing, natural disasters, and most recently, the recent COVID-19 pandemic.Updates to Microsoft Dynamics 365 Supply Chain Management in the 2020 release wave 1 help organizations proactively transform every aspect of manufacturing and supply chain operations to reduce disruptions. These new and updated capabilities drive automation and reduce downtime using IoT and mixed reality, and provide the agility to re-plan production in real time to dynamically changing ... Read More

Feed: SAS Blogs. Author: SAS Taiwan. ○ 某銀行過去在決定普卡客戶是否能升等白金卡時，總是請專員針對申請客戶的條件一一核對，判斷客戶是否可從普卡升等白金卡，但是這樣的方法： ● 浪費人力 ● 缺乏效率 ○ 於是，某銀行決定導入SAS Enterprise Miner希望能使用資料探勘的方法，從過去大量資料中： ● 找出升等白金卡的主要條件 ● 之後客戶申請時，能夠快速決定此客戶是否能升等白金卡 你該怎麼來使用SAS EM來完成主管要求的任務呢？ 沒關係，現在就依照以下流程，讓您體驗快速建模及預測： 以下為我們會使用到的資料說明(下載範例檔)※檔案有點大，請耐心等候 過去資料_檔名：creditcard.sas7bdat觀測值個數：4050590 未來資料­_檔名：creditcard_score.sas7bdat觀測值個數：834 ◎ 環境設定 使用 EM 前，需先決定專案存檔位置、資料位置並創造流程圖，所以先進行以下環境設定3步驟： Step 1. 建立專案 Step 2. 設置資料館 Step 3. 創造流程圖 建立專案(新增專案) 開啟SAS EM，選取【新增專案】→專案名稱取【EM Workshop】→【瀏覽】→選擇儲存位置【C:】→【開啟】 點選【下一步】→【完成】即完成新增專案。之後即可於儲存位置【C:】底下找到EM Workshop資料夾。 建立資料館 點選工具列【檔案】→【新增】→【資料館】→點選【建立新的資料館】→【下一步】 命名【EM】→點選【瀏覽】→找到存放SAS資料集(Data)的資料夾【Data-EMw】→【開啟】→【下一步】→【完成】 建立流程圖 左上方【流程圖】按右鍵→【建立流程圖】→命名【Workshop】→【確定】→確認右方已出現名為Workshop流程圖 ◎資料匯入- 資料說明 後續要使用過去資料(creditcard.sas7bdat) 建立模型，再使用最佳模型預測未來資料(creditcard_score.sas7bdat)，所以先將此兩個資料匯入EM中。 另外，體驗快速建模與預測時要使用建模資料 (creditcard_train.sas7bdat 與 creditcard_validate.sas7bdat) 建立模型，再使用最佳模型預測上述未來資料 (creditcard_score.sas7bdat)，所以亦先將該建模資料匯入EM中。 匯入(Creditcard) SAS資料集 對【資料來源】點選右鍵→【建立資料來源】→【下一步】→【瀏覽】→找到剛建立的SAS資料館【EM】→選擇要匯入的SAS資料集【Creditcard】→【確定】 點選【下一步】→【下一步】 【下一步】→勾選【標籤】→更改變數【角色】及【層級】→【計算摘要】 是否計算摘要【是】→【下一步】 【下一步】→【下一步】→【下一步】→【完成】。 完成後在資料來源即呈現【CREDITCARD】資料集 將【CREDITCARD】資料集拖曳至【流程圖】 匯入(Creditcard­_score) SAS資料集 對【資料來源】點選右鍵→【建立資料來源】→【下一步】→【瀏覽】→找到SAS資料館【EM】→【Creditcard­_score】→【確定】 點選【下一步】→【下一步】 【下一步】→勾選【標籤】→更改變數【角色】及【層級】→【計算摘要】 是否計算摘要【是】→【下一步】 【下一步】→將角色改為【評分】→【下一步】→【完成】→在資料來源即呈現【CREDITCARD­_SCORE】資料集 匯入(creditcard_train) SAS資料集 【資料來源】按右鍵→【建立資料來源】→【下一步】→【瀏覽】→SAS資料館【Em】→【creditcard_train】→【確定】 點選【下一步】→【下一步】 點選【進階】→【下一步】→更改變數【角色】→【重新整理摘要】→【是】→【下一步】 【下一步】到第8步→角色改為【訓練】→【下一步】 【完成】後在資料來源即呈現【creditcard_train­】資料集 匯入(creditcard_validate) SAS資料集 【資料來源】按右鍵→【建立資料來源】→【下一步】→【瀏覽】→ SAS資料館【EM】→【creditcard_validate】→【確定】 點選【下一步】→【下一步】 點選【進階】→【下一步】→更改變數【角色】→【重新整理摘要】→【是】→【下一步】 【下一步】到第8步→角色改為【驗證】→【下一步】 完成後在資料來源即呈現【creditcard_validate】資料集 已經將所需資料匯入好了！開始體驗快速建模及預測吧！ 左上方【流程圖】按右鍵→【建立流程圖】→命名【Model】→【確定】→確認已出現名為Model流程圖 將資料集creditcard_train與creditcard_validate拖曳至流程圖【Model】中。 點選工具調色盤【模型】→將【決策樹】拖曳至【流程圖】→將creditcard_train、creditcard_validate與決策樹路徑相連→將左側參數設定【最大深度】改為【3】。 點選工具調色盤【模型】→將【迴歸】拖曳至【流程圖】→將creditcard_train、creditcard_validate與迴歸路徑相連。 點選工具調色盤【評估】→將【模型比較】拖曳至【流程圖】→將決策樹、迴歸與模型比較路徑相連 點選工具調色盤【評估】→將【評分】拖曳至【流程圖】→將模型比較與評分路徑相連。 將【Creditcard­_score】資料集拖曳至【流程圖】→將Creditcard­_score與評分路徑相連→【評分】按右鍵→【執行】→執行此路徑【是】→執行完成【確定】。 ... Read More

Feed: Radar. Author: Mike Loukides. Data Governance sounds like a candidate for the most boring topic in technology: something dreamed up by middle-managers to add friction to data scientists’ lives. The funny thing about governance, though, is that it’s closely related to data discovery. And data discovery is neither dull nor additional friction; it’s an exciting process that enables great data projects, ranging from traditional reporting to artificial intelligence and machine learning. The idea of data governance originated in regulation and compliance. Not that long ago, data was a “wild west”: there were few rules and regulations about how it ... Read More

Feed: Actian. Author: Lewis Carr. In December, January, and February, I posted a short series of blogs on Flat file systems. The series of blogs focus on the continued use of flat files and why they are no longer viable for use in the future.The first installment focused on flat files and why embedded software application developers readily adopted them. Then in the second installment, I discussed why embedded developers are reluctant to use databases. The third installment looked at why developers cling to the flat file systems. The final installment provided a checklist for embedded developers migrating off flat files ... Read More

Feed: DivvyCloud. Author: Jamie Gale; Introduction and Executive Summary DevOps and the continuous integration/continuous deployment (CI/CD) pipeline are revolutionizing application development, test, and cloud delivery, enabling developers to write the application code and define the cloud infrastructure. But where is cloud security?  Unfortunately, to date, security and compliance practices have been mainly reactive, as teams scramble to catch cloud infrastructure misconfigurations, risks, and compliance violations after provisioning or creation (i.e., “at runtime”).  Relying on runtime detection increases security and compliance risks significantly. It also drives down productivity, as developers have to spend their time addressing issues in the wrong place ... Read More

Feed: AWS Big Data Blog. Apache Spark is a powerful data processing engine that gives data analyst and engineering teams easy to use APIs and tools to analyze their data, but it can be challenging for teams to manage their Python and R library dependencies. Installing every dependency that a job may need before it runs and dealing with library version conflicts is time-consuming and complicated. Amazon EMR 6.0.0 simplifies this by allowing you to use Docker images from Docker Hub and Amazon ECR to package your dependencies. This allows you to package and manage your dependencies for individual Spark ... Read More

Feed: R-bloggers. Author: R Consortium. [This article was first published on R Consortium, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Esri, international supplier of geographic information system software, web GIS and geodatabase management applications, is providing a comprehensive set of resources for researchers and others mapping the spread of the coronavirus 2019 (COVID-19) pandemic. From Esri: “As the situation surrounding coronavirus disease 2019 (COVID-19) continues to evolve, Esri is supporting our users ... Read More

Feed: Microsoft Power BI Blog | Microsoft Power BI. Author: . With the ongoing concerns of COVID-19, it’s fair to say business hasn’t been as usual for the Power BI team. All of us have been working from home for over a month now and following strict social distancing guidelines to prevent the spread of this outbreak. We hope everyone out there is staying safe and healthy in these unprecedented times. Our thoughts are with those affected and the medical professionals tirelessly working to help those in need. On the brighter side, we’re continuing to roll out features as per ... Read More

Feed: AWS Compute Blog. Author: Fred Wurden. There’s no question that organizations today are being disrupted in their industry. In a previous blog post, I shared that such disruption often accelerates organizations’ decisions to move to the cloud. When these organizations migrate to the cloud, Windows workloads are often critical to their business and these workloads require a performant, reliable, and secure cloud infrastructure. Customers tell us that reducing risk, building cloud expertise, and lowering costs are important factors when choosing that infrastructure.Today, we are announcing the general availability of the Migration Acceleration Program (MAP) for Windows, a comprehensive program ... Read More

Feed: AWS Partner Network (APN) Blog. Author: Scott Kellish. By Scott Kellish, Partner Solutions Architects at AWSBy Basil Faruqui, Principal Solutions Marketing Manager at BMC Digital Business AutomationBy Joe Goldberg, Innovation Evangelist at BMCPredictive maintenance, which analyzes sensor data to predict equipment failures, has emerged as one of the most common business use cases of machine learning (ML) and the Internet of Things (IoT). To build and train ML models, you need data science expertise. If you’re going to run those ML models in production at scale, you need data engineering expertise to build a pipeline for data ingestion, storage, ... Read More