AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Maintenance)
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This document discusses building a predictive system using machine learning. It describes predicting income using census data with four machine learning algorithms: Two-Class Decision Jungle, Two-Class Averaged Perceptron, Two-Class Bayes Point Machine, and Two-Class Locally-Deep Support Vector Machine. It also discusses tuning hyperparameters, combining results, and benchmarking performance. Additional sections cover predictive analytics processes, digital transformation, and predictive maintenance maturity models.
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AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Maintenance)
- 1. WELCOME TO: How To Use Machine Learning To Build A Predictive System By Adj Prof. Giuseppe Mascarella [email protected]
- 2. Predictive Analytics Process Predictive Modeling (Azure ML)
- 4. Target (Label): Predict Income of a person Dataset: From Census Data Algo: 1. Two-Class Decision Jungle 2. Two-Class Averaged Perceptron 3. Two-Class Bayes Point Machine 4. Two-Class Locally-Deep Support Vector Machine ML Experiment
- 5. WELCOME TO: How To Use Machine Learning To Build A Predictive System By Adj Prof. Giuseppe Mascarella [email protected] 2. Digital Transformation
- 7. Generally, learning the optimal hyperparameters for a given machine learning model requires considerable experimentation. This module supports both the initial tuning process, and cross-validation to test model accuracy: • Find optimal model parameters using a parameter sweep • Perform cross-validation during a parameter sweep Experiment: https://gallery.azure.ai/Experiment/b2bfde196e604c0aa2f7cba916fc 45c8 How to Configure and Tune Models Hyperparameters
- 9. R Script Combine results of all ML learners into a single column and to add a column with the names of the algorithms. 1. dataset <- maml.mapInputPort(1) 2. . 3. Algorithm <- c("Averaged Perceptron","Bayes Point Machine", "Decision Jungle", "Locally-Deep SVM") 4. data.set <- cbind(Algorithm, dataset) 5. . 6. maml.mapOutputPort("data.set") Benchmark Performance
- 10. Value Creation | Evolving Human + Machine Intelligence Improve Asset Utilization ($) Reduce Downtime($)43 56 Q1 Median Operational Efficiency (%) 233 301 Q1 Median 785 649 Q1 Median f(x) Helping you Grow, Improve Operations and Future Ready MACHINEHuman Market Responsiveness (Improve Forecasting, Extend Value Chain) Talented workforce Business ecosystems Data access Data and tech ecosystems Agile forms of working Machine intelligence Adaptive organizations Al-driven Operation Excellence Future Cash Flow (Fleet Maintainence, Asset Optimization) Future Ready (Sustainable Advantage) Integrated System of Intelligence <-> Gaining Advantage with Intelligence & Value Creation (Human + Machine) <->
- 11. Value Chain
- 12. AppPlat Portfolio | Customer Centric Framework to Drive Digital Transformation
- 13. Digital Transformation Microsoft, Google are focused on empowering every individual and every organization to achieve more through Digital Transformation Building better, stronger engagements by harnessing data representing a complete view of your customer, then drawing actionable intelligence, predictive insights that can deliver personalization at scale Engage your customers Reinventing products, services and business models using digital content to capitalize on emerging revenue opportunities Transform your products Optimizing operations to reshape customer relationships and service models by gathering data across a wide, dispersed set of endpoints, and drawing insights through advanced analytics that can be used to introduce improvements on a continuous, real time basis Optimize your operations Empowering employees with tools that fuel collaboration and productivity, while mitigating the risks that come with providing freedom and space to employees Empower your employees
- 14. Analytics ValueData and analytics underpin six disruptive models and certain characteristics make individual domains susceptible Indicators of potential for disruption • Assets are un-utilized due to inefficient signaling • Supply/demand mismatch • Dependence on large amounts of personalized data • Data is siloed or fragmented • Large value in combining data from multiple sources • R&D is core to the business model • Decision making is subject to human biases • Speed of decision making limited by human constraints • Large value associated with improving accuracy of prediction Domains that could be disrupted Insurance | Health care | Human capital/talent Transportation and logistics | Automotive | Smart cities and infrastructure Health care | Retail | Media | Education Banking | Insurance | Public Sector | Human capital/talent Life sciences and pharmaceuticals | Material Sciences | Technology Archetype of disruption Business models enabled by orthogonal data Hyperscale, real-time matching Radical personalization Massive data integration capabilities Data-driven discovery Enhanced decision making Smart Cities | Health care | Insurance | Human capital/intent
- 15. Leading Digital Transformation Through Proven Customer Use Cases Improving visibility and making accurate predictions Getting the right products to the right places Offering customers exactly what they want, when they want it Fixing problems proactively before they start Exploring new business opportunities Remote monitoring Inventory management Demand forecasting Supply chain optimization Risk and compliance management Marketing mix optimization Personalized offers Product recommendations New product introduction Predictive maintenance Operational efficiency Customer service improvement Cross-sell and upsell Product-as-a-service New data-driven services
- 16. WELCOME TO: How To Use Machine Learning To Build A Predictive System By Adj Prof. Giuseppe Mascarella [email protected] 3. CMM (Costumer Maturity Model)
- 17. Predictive Maintenance | As The Capabilities To Know Your Asset Increase, Costs Decreases and OEE PerformancesTake Off Reactive / Informative Predictive Transformative Asset Utilization and Maintenance • Avoid unexpected downtime • Avoid over- and under- maintenance • Operate Asset as a Service • Take preemptive corrective actions Overall Equipment Effectiveness (OEE) • Create New Business Models Cost of Incidents and Maintenance • Free up Working Capital, Margin Contribution • Provide Global Support Intelligence Reports ERP, Maint. Data • Value Creation through Human and Machine Intelligence
- 18. CMM(Customer Maturity Model) of Predictive Maintenance | Capability Model Predictive Maintenance Stage 1: Reactive (Report) Stage 2: Insights Stage 3: Predictive/ ML Stage 4: Transformative Stage 5: Game Changer OUTCOMES Vision Schedule and manage using past operational and routine performance data Analyze conditions and make informed decisions Discover new insight, and predict likelihood and timeframe of failures Transform the experience with real-time insight, actions and continuous feedback Shape new business models with digital ecosystem Strategic Intent • Define operational rhythm • Meet SLAs, compliance and warranty conditions • Orchestrate and leverage readily available reports and operational observations • Become purpose-driven with connected, complete, correct and connected data • Model asset-specific plans based on the asset condition • Easy access to insights on the whys and the trends • Manage the Voice of the Asset • Instrument the assets to provide real-time data on factors affecting asset condition • Predict and schedule maintenance for desired operations • Operate Asset as a Service by altering the asset behavior in real-time • Take corrective actions before a potential failure • Predict and perform maintenance based on the business impact • Launch digital services, leveraging design, data and delivery insight • Create new customer experiences and solutions, integrating partner assets • Monetize learning KPIs • Unplanned downtime • Regulatory compliance • Maintenance time and costs • Time between failures • Spare parts inventory • Annual budget • Asset utilization • Unexpected breakdowns • Capital and resource investment • Global reach • Revenue or throughput per asset • Customer loyalty • Outcome-based pricing • New markets • Cross-selling • Eco-system maturity CAPABILITIES: Data, Intelligence and Actions APPROACH: Architecture Directions
- 19. Predictive Maintenance | Capability Profile Across Maturity Levels Predictive Maintenance Stage 1: Reactive Stage 2: Informative Stage 3: Predictive Stage 4: Transformative Stage 5: Game Changer CAPABILITIES Data (Sources, time, quality, access) • Manufacturers reports • Asset features • Failures/repairs reports • Historical data from operational systems • Intermittent updates • Asset condition data • Correlated quality, ERP, and operational data • Scheduled data queries and data polling • Real-time, streaming data about asset conditions, environmental factors, and operating conditions • Multisite data aggregation • Data readiness for data science • Cognitive and feedback data • Business process / workflow • Organization data e.g. operator’s skills • Events, Smart sensing • Ecosystem data and services • External context (customer, consumer) • Real-time capability and data discovery Intelligence (Interpretations, analytics, insights, learnings) • Web-based reports, dashboards • Data visualization of historical and operational data • Self-service analytics • Asset condition monitoring and assessment • Statistical modeling • Trend analysis and forecasting • Predictions using data mining, modeling and algorithms across all data • Stream analytics • Rolling aggregates, analysis and recommendations • Insight at sensor and interface levels • Deep learning e.g. vibrations • Real-time predictions using current business context and operating conditions • Analyze current state behavior across ecosystem and identify opportunities • Evaluate health of data and algorithms and predict adjustments Actions (New or change in activities) • Inventory assets • Develop plans and schedule maintenance for assets based on past performance • Plan and schedule resources • Forecast and optimize schedule and inventory • Manage critical assets and business operations • Manage planned downtime • Manage resource productivity • Create knowledgebase • Check health while in use • Identify potential causes and time window, and take proactive actions • Generate alerts and propose best actions • Support remotely • Reliability engineering • Self-identify alternate paths for continuous operations • Heal the asset while in use • Create outcome-based business processes and customer experience • Make every interaction a source of revenue • Productize data, intelligence, algorithms, and business processes • Integrate partner services • Create BOTs APPROACH Architecture Directions • Systems of Records • Client/server or distributed architecture • Data marts • Reporting and analytics • Systems of Engagement • Service-oriented architecture • Integration • Data warehouses • Analytical modeling • Systems of Inference • Lambda architecture • NoSQL • Data lakes • Cloud • Systems of Learning • Neural network and FOG architecture • Cognitive services • In memory, edge analytics • Systems of Digital Markets • Microservices architecture • APIs
- 20. WELCOME TO: How To Use Machine Learning To Build A Predictive System By Adj Prof. Giuseppe Mascarella [email protected]