PDF-Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning

Prepare for Microsoft Exam 707748211and help demonstrate your realworld mastery of performing key data science activities with Azure Machine Learning services Designed for experienced IT professionals ready to advance their status Exam Ref focuses on the critical thinking and decisionmaking acumen needed for success at the MCSA level Focus on the expertise measured by these objectivesPrepare data for analysis in Azure Machine Learning and export from Azure Machine LearningDevelop machine learning modelsOperationalize and manage Azure Machine Learning ServicesUse other services for machine learningThis Microsoft Exam RefOrganizes its coverage by exam objectivesFeatures strategic whatif scenarios to challenge youAssumes you are familiar with Azure data services machine learning concepts and common data science processesAbout the Exam Exam 70774 focuses on skills and knowledge needed to prepare data for analysis with Azure Machine Learning find key variables describing your data8217s behavior develop models and identify optimal algorithms train validate deploy manage and consume Azure Machine Learning Models and leverage related services and APIs About Microsoft Certification Passing this exam as well as Exam 70773 Analyzing Big Data with Microsoft R earns your MCSA Machine Learning certifi191cation demonstrating your expertise in operationalizing Microsoft Azure machine learning and Big Data with R Server and SQL R Services See full details at microsoftcomlearningnbsp. DCIM-B387. Ramesh Chinta Principal GPM, Azure. Krishna Anumalasetty Principal PM Lead, Azure. Data protection – challenges. 2. Data is growing. Data is everywhere. Threats/Attacks growing. Title:. Microsoft Azure General Overview. Current scope:. Length:. 45-60 minutes (Customize as necessary depending on audience). (DO NOT SHOW). Micrsoft. Azure Enterprise Deck. Content Level:. Level . On-Premises . Applications. Yousef A. Khalidi. Distinguished Engineer. Microsoft Corporation. Why Embrace the Cloud?. Greater agility. Reduced cost. Enable new scenarios. Cloud as communication hub. Data sharing across devices. <Presenter name>. <Presenter role>. Objectives. 8/15/2014. 2. Solution Overview. Your Scenario. Microsoft Azure Overview. Solution Alignment. Microsoft Azure Compute – Infrastructure as a Service (IaaS) Virtual Machines. Prabhat. Data Day. August 22, 2016. Roadmap. Why you should care about Machine Learning?. Trends in Industry. Trends in Science . What is Machine Learning?. Taxonomy. Methods. Tools (Evan . Racah. ). Venkat. . Krishnamachari. , Damian Andre. BRK2156. Delivering Disaster Recovery Leveraging Public Cloud Infrastructure. Venkat Krishnamachari, Damian Andre. CRITICAL. APPLICATIONS. are moving to public cloud. with Eliezer Kanal and Brian . Lindauer. Copyright 2016 Carnegie Mellon University. This material is based upon work funded and supported by the Department of Defense under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center.. What is Azure?. Connecting with Longer Wires. Azure by the numbers. 1.4 Million. SQL databases. hosted. 500. Million. Azure AD users. 57%. of Fortune 500 . companies. 60. Billion. Hits to hosted websites. Table of Contents. Overview. Scenario Value. Technical Overview. Components/Capabilities. Reference Architecture/Design Patterns. Waves of innovation. Cloud. Globally available, unlimited compute resources. Blog: Wordpress.sqlrican.com. Twitter: @. Sqlrican. Introduction to Azure Machine Learning Studio. Azure Machine Learning product overview. Azure ML development process. Demo – build and deploy a predictive model. A Tale of Two clouds 2016-11-16 Bill Zack Blue Chip Consulting Group Senior Solution Architect bill.zack@bluechip-llc.com Blog: CloudyInNashville.com A Feature Comparison of Windows Azure and Amazon Web Services for. Jianlin Cheng, PhD. Computer Science Department, University of Missouri, Columbia. Center. Importance of Machine Learning and Data Mining. Computer Science . (AI, database, robotics, vision, image processing, . It’s no secret that this world we live in can be pretty stressful sometimes. If you find yourself feeling out-of-sorts, pick up a book.According to a recent study, reading can significantly reduce stress levels. In as little as six minutes, you can reduce your stress levels by 68%. Sylvia Unwin. Faculty, Program Chair. Assistant Dean, iBIT. Machine Learning. Attended TDWI in Oct 2017. Focus on Machine Learning, Data Science, Python, AI. Started with a catchy opening speech – “BS-Free AI For Business”.