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Summit Australia 2019 - Supercharge PowerPlatform with AI - Dipankar Bhattacharya & Andrew Ly

  1. CPP07: Supercharge Power Platform with AI Dipankar Bhattacharya (KPMG) & Andrew Ly (KPMG)
  2. Speakers
  3. Dipankar Bhattacharya Associate Director KPMG Andrew Ly (MVP) Associate Director KPMG
  4. Agenda
  5. The AI Impact The AI Landscape Built-in AI with PowerPlatform Microsoft Azure AI & ML Custom AI for PowerPlatform ARTIFICIAL INTELLIGENCE
  6. The AI Impact
  7. TOP SECTORS ADOPTING THIS TECHNOLOGY THREE FACTORS ENABLING AI GROWTH The overall artificial intelligence market is expected to reach US$16.06B by 2022 Natural language processing is expected to hold the largest market share by 2022 AI & Machine Learning: A Forecast
  8. Reasons for adopting AI AI will allow us to obtain or sustain a competitive advantage Why is your organisation interested in AI? * AI will allow us to move into new businesses Incumbent competitors will use AI New organisations using AI will enter our market Pressure to reduce costs will require us to use AI Pressure to reduce costs will require us to use AI Customers will ask for AI-driven offerings 84% 75% 75% 69% 63% 61% 59% * MIT Sloan Management School Study
  9. AI in Customer Experience Artificial Intelligent 0 0.5 1 Emotion strength Neutral Boredom Joy Emotions found to have the single greatest impact on customer decisions & customer experience “If you don’t understand their emotions, you don’t understand your customers.” -Forrester Research Group
  10. The AI Landscape
  11. Definition Artificial Intelligence (AI) is the intelligence of machines and the branch of computer science which aims to create it. definition: intelligence • Intelligence (noun) - 1. the ability to acquire and apply knowledge and skills - 2. a person with this ability - 3. the gathering of information of military or political value - ORIGIN ME: via Ofr. From L. intelligentia, from intelligere ‘understand’, var. of intellegere ‘understand’, from inter ‘between’ + legere ‘choose’
  12. AI Landscape ARTIFICIAL INTELLIGENCE A program that can sense, reason, act and adapt. MACHINE LEARNING Algorithms whose performance improve as they as exposed to more data over time. DEEP LEARNING Subset of machine learning in which multilayered neural networks learn from vast amounts of data 1950’s 1960’s 1970’s 1980’s 1990’s 2000’s 2010’s
  13. AI Architecture
  14. Machine Learning Machine Learning Algorithms Supervised Learning Regression Predict continuous valued output [e.g. Predicting Stock Price or House Price] Classification Predict Discrete valued output (e.g. 0 or 1) Two- class classific ation Multi- class classificat ion All data is labelled and the algorithms learn to predict the output from the input data. [classifying new data from known properties] [e.g. historical stock prices can be used to hazard guesses at future prices.] Unsupervised Learning All data is unlabelled and the algorithms learn to inherent structure from the input data [discovering hidden properties of data] Clustering Discover the inherent groupings in the data, such as grouping customers by purchasing behaviour. Anomaly Detection Identification of items or events that do not conform to an expected pattern or to other items present in a dataset. [fraud detection, for example, any highly unusual credit card spending patterns] Reinforcement Learning Allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance. [making the best decisions now to maximize long-term reward ] [common in robotics, where the set of sensor readings at one point in time is a data point, and the algorithm must choose the robot's next action. ] [It is also a natural fit for Internet of Things applications.] Machine Learning: Field of study that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959)
  15. Built-in AI with PowerPlatform: AI Builder
  16. /r/ProgrammerHumor
  17. What is AI Builder? • Enables creation of AI models without code • Easy training of AI models • Easy use of AI models within PowerApps & Flow • Currently in Preview • GA October 2019
  18. AI Model Types • Can a machine predict what a value will be? • Can a machine reliably read a form? • Can a machine understand an image? • Can a machine classify text objects? • How much historical data will I need? • How much can a form deviate? • How many images is used to train this model? • What classifications can be applied? Binary Classification Forms Processing Object Detection Text Classification
  19. Get Started Today
  20. Microsoft Azure AI and ML
  21. Microsoft AI Services INFRASTRUCTURE CPU, FPGA, GPU Cosmos DB SQL DB SQL DW Data Lake Spark DSVM Batch AI ACS Edge AI ON DATA AI COMPUTE TOOLS CODING & MANAGEMENT TOOLS VS Tools for AI Azure ML Studio Azure ML Workbench DEEP LEARNING FRAMEWORKS Cognitive Toolkit TensorFlow Caffe Others (Pycharm, Jupyter Notebooks…) Others (Scikit-learn, MXNet, Keras, Chainer, Gluon…) Prebuilt AI (Azure Cognitive Services) Conversational AI (Azure Bot Service) Custom AI (Azure Machine Learning)
  22. Microsoft AI
  23. Azure ML Algorithms Is this A or B? Classification Algorithms How much? How Many? Regression Algorithms Is this weird? Anomaly Detection How is this organised? Clustering Algorithms What should I do now? Reinforcement Learning Algorithms Which brings in more customers: a $5 coupon or a 25% discount? What will my fourth quarter sales be? Which printer models fail the same way? If you have a car with pressure gauges, you might want to know: Is this pressure gauge reading normal? For a robot vacuum: Keep vacuuming, or go back to the charging station?
  24. Azure ML Studio
  25. Custom AI for PowerPlatform
  26. What algorithm to use? The choice of a model affects (and is affected by) • Whether the model meets the business goal • How much pre-processing the model needs • How accurate the model is • How explainable the model is • How fast the model is (in making predictions) • How scalable the model is (building and predicting)
  27. Deploying modelsIs your data ready? Getting ready Define Objective Access and Understand the data Pre-processing Historical Data [features + labels] Split Training Data [features + labels] Testing Data [features + labels] Train Model [model learns from training data] Score Model [model predicts on testing data] Evaluate Model [compare predicted results and true labels] Future Data [features only] Score Model [model predicts the future data] Prediction Results Choosing and Tuning models
  28. Data Import from CDS to Azure ML Security Model … Azure Data Lake Storage Gen 2 COMMONDATA MODEL Standard Entities Contact Account First Party Entities Lead Opportunity Custom Entities Donation Membership ISV Entities Email Send Web Form Business Logic Plugins Sync Workflows Calculated and Rollup fields COMMONDATASERVICE Flows POWER PLATFORM Model Driven Apps Canvas Apps Admin & Monitoring Power BI Tenant Workspaces Dashboards Workbooks Reports Datasets Data Flow CDS SDK First Party Apps ISV Power Apps Power User Companion Apps Azure Data Services CDSConnectors
  29. Is your data ready? Is your data relevant? Do you have connected data? Is your data accurate? Do you have enough data to work with?
  30. Deploying models – Model consumption It’s important to know (as much as possible) how models are to be consumed:  A model that is consumed by a web app (like Dynamics 365) needs to be fast  A model that is used to predict in batch (e.g. building a Marketing segmentation based on some prediction) needs to be scalable  A model that updates a dashboard, as data streams in, may need to be fast and scalable
  31. Demo