Integrating Azure Machine Learning and Predictive Analytics with SharePoint Online
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The document discusses a SharePoint conference presentation by Bhakthi Liyanage on machine learning, emphasizing its definition and various applications within data analysis. It covers the machine learning lifecycle, from defining objectives and collecting data to training and evaluating models, showcasing the importance of proper data preparation and integration. Additionally, it highlights Azure Machine Learning as a powerful tool that enables rapid deployment of predictive analytics solutions.
Integrating Azure Machine Learning and Predictive Analytics with SharePoint Online
- 1. Bhakthi Liyanage SharePoint Saturday Charlotte 17 September 2016
- 2. @CASPUG #SPSCLT16 Platinum Gold SilverandBronze Charlotte SharePoint Community! ď§ ď§ ď§ THANK YOU EVENT SPONSORS!
- 3. @CASPUG #SPSCLT16 PLEASE TELL US WHAT YOU THINK http://bit.do/SPSCLT16
- 4. @CASPUG #SPSCLT16 CONFERENCE COMMUNICATION ď§ lanyrd.com/2016/spsclt16 ď§ @CASPUG #SPSCLT16 ď§ [email protected]
- 5. ⢠Who am I? ⢠Introducing machine learning ⢠Introducing Azure Machine Learning ⢠Machine Learning Lifecycle ⢠Demo ⢠Summary ⢠Q & A
- 6. 6 Sr. SharePoint Architect 16+ years in the IT industry 11+ years in SharePoint [email protected] @bhakthil https://www.linkedin.com/pub/bhakthi- liyanage/14/15/912 https://github.com/bhakthil
- 8. Academic Definition Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Simple Definition Computing systems that become smarter with learning and experience Experience = Past data + human input
- 9. ⢠Need to know of the future
- 10. ⢠Being able to predict the future with a reasonable accuracy Reports Yesterday Today Tomorrow Business Intelligence Predictive Analytics Predictability Time
- 11. A highly educated and skilled person who can solve complex data problems by employing deep expertise in scientific disciplines (mathematics, statistics or computer science) A skilled person who creates or maintains data systems, data solutions, or implements predictive modelling Roles: Database Administrator, Database Developer, or BI Developer A skilled person who designs and develops programming logic, and can apply machine learning to integrate predictive functionality into applications
- 12. ď˝ What problems are we trying to solve? ⌠Anomaly detection ⌠Customer churn ⌠Predictive maintenance ⌠Recommendations system ď˝ What data do we have or do we have any data at all? ⌠Data already available via sensory systems, transactional databases, customer sales databases, etc. Predictive maintenance Vision Analytics Recommenda- tion engines Advertising analysis Weather forecasting for business planning Social network analysis Legal discovery and document archiving Pricing analysis Fraud detection Churn analysis Equipment monitoring Location- based tracking and services Personalized Insurance
- 13. ď˝ Data Consist of ⌠Features (aka input parameters) : The data that is fed in to the model ⌠Identify which features relevant for the problem ⌠Labels : Historical result of each observation ď˝ Training Data ⌠Pairing of features and label ⌠Historical ď˝ Data Validation ⌠Used to verify the trained model
- 14. ď˝ Supervised ⌠Machine learning task of inferring a function/model from labeled training data or examples ⌠Training data consist of both features and labels ď˝ Un-supervised ⌠Machine learning task of inferring a function to describe hidden structure from unlabeled data ⌠Data contains only features
- 16. ď˝ Enables powerful cloud-based predictive analytics ď˝ Professionals can easily build, deploy and share advanced analytics solutions ď Browser based, Rapid Deployment ď Connects seamlessly with other Azure data-related services, including: ď Azure HDInsight (Big Data) ď Azure SQL Database, and ď Virtual Machines ď Models are consumed via ML API service
- 18. ď˝ It is important to start a machine learning project with a clearly defined objective I need to predict customer churn rate for next 6 months⌠Define Objective I need to suggest relevant products to the customers I need to know when my manufacturing equipment will fail
- 19. ď˝ Collecting complete data is critical ⌠Garbage in âş Garbage out ď ď˝ Datasets can be sourced from: ⌠Internal sources, i.e. operational systems, data warehouse, etc. ⌠External sources ⌠Different formats, i.e. relational, multidimensional, text, map- reduce ď˝ Combining datasets can enrich data ⌠E.g., integrate internal data to external data like weather, or market intelligence data ⌠Weather data with flight delay data ⌠Population data with energy consumption data Collect Data
- 20. ď˝ Prepare data for machine learning ⌠Transform to cleanse, reduce or reformat ⌠Isolate and flag abnormal data ⌠Appropriately substitute missing values ⌠Categorize continuous values into ranges ⌠Normalize continuous values between 0 and 1 ď˝ Of course, having the required data to begin with is important ⌠When designing systems, give consideration to attributes that may be required as inputs for future modeling, e.g. demographic data: Birth date, gender, etc. Prepare Data
- 21. ď˝ This stage is iterative, and experimentation involves: ⌠Selecting a machine learning algorithm ⌠Defining inputs and outputs ⌠Optimizing by configuring algorithm parameters ď˝ Model evaluation is critical to determine: ⌠Accuracy, Reliability, Usefulness Train Models Evaluate Models
- 22. ď˝ First, add a scoring experiment â Training logic is replaced with a trained model â Inputs and output end-points are added â Module properties can be parameterized ď˝ Publish the experiment to the gallery â Learn from others by discovering experiments â Contribute and showcase your experiments Deploy
- 23. Integrate ď˝ Integrate the experiment with external applications â Integration offers REST web service end points â Each web service offers two methods: ⢠Request/Response Service (RRS) âş Low latency, highly scalable web service ⢠Batch Execution Service (BES) âş High volume, asynchronous scoring of many records
- 24. Stream analytics, blob storage, Azure SQL, HDInsight Azure ML Services Clients Azure ML Studio ML web service end- points Data Model Development Model Deployment Operationalize
- 25. Power BI/DashboardsMobile AppsWeb Apps Azure Portal Azure Ops Team ML Studio Data Scientist HDInsight Azure Storage Desktop Data Azure Portal & ML API service Azure Ops Team ML API service Developer ML Studio and the Data Professional ⢠Access and prepare data ⢠Create, test and train models ⢠Collaborate ⢠One click to stage for production via the API service AzurePortal&MLAPIservice and the Azure Ops Team ⢠Create ML Studio workspace ⢠Assign storage account(s) ⢠Monitor ML consumption ⢠See alerts when model is ready ⢠Deploy models to web service ML API service and the Application Developer ⢠Tested models available as a URL that can be called from any endpoint Business users easily access results from anywhere, on any device
- 27. Machine Learning is a subfield of computer science and statistics that deals with the construction and study of systems that can learn from data. Azure Machine Learning key attributes: Fully managed âş No hardware or software to buy Integrated âş Drag, drop, connect and configure Best-in-class algorithms âş Proven solutions from Xbox and Bing R built in âş Use over 400 R packages, or bring your own R or Python code Deploy in minutes âş Operationalize with a click Flexible consumption âş Any device capable of consuming REST API Machine Learning is now approachable to developers
- 28. JOIN US FOR SHAREPINT ď˝ Immediately following todayâs event ď˝ First drink is on us ď˝ Brink your event ticket for validation ď˝ Duckworthâs Grill & Taphouse 330 North Tryon Street Charlotte, NC 28202 (7th and Tryon)
- 29. Q & A