Data Science Innovation Summit Philadelphia 2019 - pariveda
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Pariveda's Ryan Gross presented on the ways that companies are transforming themselves using data and data science. Many of the challenges that organizations run into are cultural and/or process related. The presentation goes through a framework for getting your organization started successfully with Data Science.
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Data Science Innovation Summit Philadelphia 2019 - pariveda
- 1. 1 © Pariveda Solutions. Confidential & Proprietary. Principal Architect, Chicago Worked with a couple of the Fortune 500 to use data as an asset, and others to build AWS solutions and strategies. 10 years of technology leadership with Pariveda Solutions and General Electric Cloud architect, big data wrangler, professional hobbyist, amateur cyclist, and lifelong Pittsburgh Steelers fan. Entertainer to many, and entertained by life’s mysteries. RYAN Gross
- 2. Table of Contents Machine Learning and The Business Lay the Foundation with Data Engineering Operationalizing Data Science Questions Appendix
- 3. 3 © Pariveda Solutions. Confidential & Proprietary. from automation, improved asset utilization, DevOps optimization, etc. Net valuation of the world’s largest companies
- 4. 4 © Pariveda Solutions. Confidential & Proprietary. from automation, improved asset utilization, DevOps optimization, etc. Net valuation of the world’s largest companies
- 5. 5 © Pariveda Solutions. Confidential & Proprietary. from automation, improved asset utilization, DevOps optimization, etc. Net valuation of the world’s largest companies
- 6. 6 © Pariveda Solutions. Confidential & Proprietary. from automation, improved asset utilization, DevOps optimization, etc. L U S E O N L Y D O N O T D S T R B U T E
- 7. 7 © Pariveda Solutions. Confidential & Proprietary. VAPORIZED ML as a Vaporizing Agent What does it mean to be ‘Vaporized’? The Software- Defined Society
- 8. 8 © Pariveda Solutions. Confidential & Proprietary. Democratizing Machine Learning Automatically build and evaluate hundreds of models in parallel Where state of the art algorithms are always live and accessible to anyone Accelerate the development and delivery of models with infrastructure automation, seamless collaboration, and automated reproducibility Build, train, and deploy machine learning models at scale
- 9. 9 © Pariveda Solutions. Confidential & Proprietary. Democratizing Machine Learning
- 10. 10 © Pariveda Solutions. Confidential & Proprietary. ORGANIZATIONS ARE SOLVING REAL-WORLD PROBLEMS WITH MACHINE LEARNING Pediatrics hospital predicts individual patients risk of contracting Central Line-Associated Blood Stream Infections (CLBASIs) up to 3 days earlier than normal infection detection, allowing them to take action to minimize this risk Oilfield equipment provider predicts the win/loss probability of bids based on market conditions, customer information, and historical sales data. They can easily see the impact of different pricing strategies, speeding the quoting process and increasing likelihood of a sale Retail energy company forecasts the electricity usage of individual customers instead of aggregate groups. Customers are provided with an increased awareness of their upcoming energy costs and the energy company can better hedge energy prices Heavy equipment manufacturer predicts that a part is going to fail up to 3 weeks before failure. This allows customers avoid expensive lost productivity due to unexpected downtime. Customers also reduce maintenance costs by only replacing parts as they are about to fail, rather than 20% of parts during each maintenance window
- 11. • Building Data Lake Platforms & Data Governance processes to make data available • Change management being utilized to adopt ML solutions across the business • Filling out roles on Data Science team • Business actively looking • Executing against a value- driven backlog of ML opportunities • Scaling up the data science function, supplementing with platforms • Business understands data- driven decision making, • Utilizing controlled experiments during roll-out • Putting first Machine Learning MVP solutions into production • Educating the business on utilizing ML predictions • Sourcing data for ML models ad-hoc • Hiring leadership roles on data science team • Not currently in position to build a production machine learning solution. • Require major Data Engineering to get ready. • Can also implement POCs where data is ready to show the benefits of ML to executives STORMINGFORMING NORMING PERFORMIN G Readiness for DS Value Realization
- 12. 12 © Pariveda Solutions. Confidential & Proprietary. Software Engineering Data Engineering Data Science Data Scientists spend ~80% of their time on activities that are not core to their skills sets. A team with varied skills can help focus scarce Data Science resources. Building a Value Realization Team $$$ $$ $$ Business Analysis
- 13. 13 © Pariveda Solutions. Confidential & Proprietary. Building a Value Realization Pipeline Metrics Prototypes & LEARN Identify, Assess, and Prioritize Experiment and Learn Insights Opportunities Deploy, Test, and Run
- 14. 14 © Pariveda Solutions. Confidential & Proprietary. Building a Value Realization Pipeline Sprint Release Planning Weekly Work Item Conversation Design Develop Test Peer Review Regression Testing Acceptance Testing Deploy to Dev Code Analysis Iteration Planning Backlog Grooming Status Meeting Deploy to Test Visual Design System Docs & Test Scripts Code Review Facilitated UAT User Validation QA Testing Potentially Shippable Product Iteration Review Retro Deploy to Stage Automated UI Regression Testing API Load & Performance Testing Integration& Performance Testing Holistic Usability Testing Deploy to Production Product Release Conceptualization Learn & Repeat Identify, Assess, and Prioritize Business Focused Leveraged Use Cases Opportunity Mapping Process Impact Technical Complexity Business Value Value Mapping Generate Concept Cards Research Spike Learn & Repeat Business Assessment Data Availability Assessment Operationaliza tion Assessment Data Acquisition Data Understanding Data Validationand Cleanup Feature Engineering Model Development Model Evaluation Deployment Spike Learn & Repeat Testing Deployment Change Management Monitoring Automation Performance Tuning Experiment and Learn Deploy, Test, and Run Concept Card Review Research Spike Results Deployment Spike Results User Stories Progress Concept Cards Pivot | Persevere | Promote | Quit Pivot | Persevere | Promote | Quit Software Engineering Data Engineering Data Science$$$ $$ $$ Business Analysis
- 15. Table of Contents Machine Learning and The Business Lay the Foundation with Data Engineering Operationalizing Data Science Questions Appendix
- 16. 16 © Pariveda Solutions. Confidential & Proprietary.
- 17. 17 © Pariveda Solutions. Confidential & Proprietary.
- 18. 18 © Pariveda Solutions. Confidential & Proprietary. V V VVolume Velocity Variety
- 20. 20 © Pariveda Solutions. Confidential & Proprietary.
- 21. 21 © Pariveda Solutions. Confidential & Proprietary. V V V V VVolume Velocity Variety Veracity Value
- 22. 22 © Pariveda Solutions. Confidential & Proprietary. Data Forge: Modern Data Pipeline
- 23. 23 © Pariveda Solutions. Confidential & Proprietary. LEVERAGING YOUR DATA PLATFORM ENABLING TECHNOLOGIES ENABLING CAPABILITIES OUTCOMES A well structured and maintained data lake supports and integrates better with enabling technologies and implemented use cases Validation Engine Data Platform Analytics Data Catalog Lineage Tracker Notifications / Logging Directory Service Integration Data Provisioning Infrastructure as Code Structure Formats New Data Product Development Maintenance and Operations Data Governance Experimentation AgilityData Quality Security Return on Investment Visibility Compliance Technology People Process
- 24. 24 © Pariveda Solutions. Confidential & Proprietary. Data Forge: Modern Data Governance GOVERNANCE IS NOT A PROJECT It is a level of rigor that must be applied continuously. Motivation to govern is driven by demonstrating the value of the data. The activities of governance have not changed, but they are applied as needed. EXPERIMENTATION MUST BE ENABLED Governance shouldn’t hinder the ability to experiment with new ideas quickly. It should be applied after the value of the data has been established. Raw data should be easy to discover, access, and understand Traditional Governance (75% failure rate) Value Driven Governance
- 25. 25 © Pariveda Solutions. Confidential & Proprietary. Data Forge: Experiment to Show Value Data Lab Experimentation Platform Prototypes DATA LAKE
- 26. Table of Contents Machine Learning and The Business Lay the Foundation with Data Engineering Operationalizing Data Science Questions Appendix
- 27. 28 © Pariveda Solutions. Confidential & Proprietary.
- 28. 29 © Pariveda Solutions. Confidential & Proprietary. IDEA Engage internal stakeholders, capture new business ideas and fill-in Concept Cards Ideate - Create Concept Cards GOOD IDEAReview Concept Cards w/ Executive Leadership, prioritize and select concepts to research further Review & Prioritize REAL IDEA Engage external & related parties to formalize dependencies; Develop mock-ups and/or prototypes Research & Prototype VIABLE IDEA Fully spike prototype for subset of customers or users Focused/Market Test Production launch of product/service after incorporating lessons learned from Market Test Release CONCEPT “LINE OF FEASIBILITY” REALITY TO DEVELOPMENT PHASES IDEA MATURATIO N VALUABLE IDEA Moving Data Science to Production
- 29. 30 © Pariveda Solutions. Confidential & Proprietary. Set up RESTful APIs to score new examples and retrain your model periodically if necessary ModelOps: Deploying a Machine Learning Model to Production Hey, I need a prediction!
- 30. 31 © Pariveda Solutions. Confidential & Proprietary. Set up RESTful APIs to score new examples and retrain your model periodically if necessary ModelOps: Deploying a Machine Learning Model to Production Machine Learning Model Hey, I need a prediction! Step 1 Do some manual data science and create a predictive model
- 31. 32 © Pariveda Solutions. Confidential & Proprietary. Set up RESTful APIs to score new examples and retrain your model periodically if necessary ModelOps: Deploying a Machine Learning Model to Production Machine Learning Model Hey, I need a prediction! Score Step 1 Do some manual data science and create a predictive model Step 2 Deploy a score API that can return predictions for users and calling applications
- 32. 33 © Pariveda Solutions. Confidential & Proprietary. Set up RESTful APIs to score new examples and retrain your model periodically if necessary ModelOps: Deploying a Machine Learning Model to Production Machine Learning Model Hey, I need a prediction! Score Step 1 Do some manual data science and create a predictive model Time to update our model! Train, Validate, Deploy Step 2 Deploy a score API that can return predictions for users and calling applications Step 3 Deploy an API that can retrain, validate, and deploy models and potentially setup a timer job to hit that API
- 33. 34 © Pariveda Solutions. Confidential & Proprietary. Set up RESTful APIs to score new examples and retrain your model periodically if necessary ModelOps: Deploying a Machine Learning Model to Production Machine Learning Model Hey, I need a prediction! Score Step 1 Do some manual data science and create a predictive model Time to update our model! Train, Validate, Deploy Step 2 Deploy a score API that can return predictions for users and calling applications Step 3 Deploy an API that can retrain, validate, and deploy models and potentially setup a timer job to hit that API
- 34. 35 © Pariveda Solutions. Confidential & Proprietary. Enhanced Sales Transactions Weighted Average Sales by customer (Daily) Weighted Average Sales (Weekly, Monthly, etc.)Buying pattern by customer (used for fraud in Enhance) Enhanced Product Manufacturing Records Average cost per product by location Weather report and predictions Weather impact factor by customer Predicted Customer Revenue (Daily) Predicted Customer Profit (Daily) … ModelOps: Chaining a Machine Learning Model in Production You can use the modern data platform to monitor problems and alert or act on them Weighted Average Sales by customer (Hourly) Weighted Average Sales (Daily, Weekly, Monthly)
- 35. 36 © Pariveda Solutions. Confidential & Proprietary. Prediction Accuracy: Customer Revenue (Weekly, Monthly) Enhanced Sales Transactions Weighted Average Sales by customer (Daily) Weighted Average Sales (Weekly, Monthly, etc.)Buying pattern by customer (used for fraud in Enhance) Enhanced Product Manufacturing Records Average cost per product by location Weather report and predictions Weather impact factor by customer Predicted Customer Revenue (Daily) Predicted Customer Revenue (Weekly, Monthly) Predicted Customer Profit (Daily) … ModelOps: Monitoring a Machine Learning Model in Production You can use the modern data platform to monitor problems and alert or act on them Weighted Average Sales by customer (Hourly) Weighted Average Sales (Daily, Weekly, Monthly)
- 36. 37 © Pariveda Solutions. Confidential & Proprietary. Prediction Accuracy: Customer Revenue (Weekly, Monthly) Enhanced Sales Transactions Weighted Average Sales by customer (Daily) Weighted Average Sales (Weekly, Monthly, etc.)Buying pattern by customer (used for fraud in Enhance) Enhanced Product Manufacturing Records Average cost per product by location Weather report and predictions Weather impact factor by customer Predicted Customer Revenue (Daily) Predicted Customer Revenue (Weekly, Monthly) Predicted Customer Profit (Daily) … ModelOps: Monitoring a Machine Learning Model in Production You can use the modern data platform to monitor problems and alert or act on them Train, Validate, Deploy Alert the team! Weighted Average Sales by customer (Hourly) Weighted Average Sales (Daily, Weekly, Monthly)
- 37. 38 © Pariveda Solutions. Confidential & Proprietary. ModelOps: Model-Building Flow w/ Artifact Management Manage notebook development in source control Use S3 Object Versioning to Ensure Data Consistency Build Docker Images for specific algorithms in Code Build, store in ECR Manage model artifacts in S3 using artifact repository structure (ivy, maven) Capture data transformations from notebooks for production as either lambda step functions or EMR Steps Leverage canary deployment features of the platform to test real- world effectiveness before cutting over
- 38. Table of Contents Machine Learning and The Business Lay the Foundation with Data Engineering Operationalizing Data Science Questions Appendix
- 39. 40 © Pariveda Solutions. Confidential & Proprietary.
Notas do Editor
- http://mattturck.com/the-power-of-data-network-effects/
- Organizations now collect an enormous volume of data about their customers, products, processes Those organizations with the capability to turn data into actionable insights leap ahead of their competitors – delighting customers, reducing costs, opening new markets Human experts and traditional decision support tools become overwhelmed as the amount of data increases Distinguishing subtle differences across hundreds or thousands of interacting factors is difficult The sheer volume of data to be analyzed becomes a limiting factor Machine learning is being used by a rapidly increasing number of organizations to overcome the challenge of generating insights from large data sets Cheap computing power and easy accessibility to advanced algorithms has reduced the barrier to entry – ML is not just for academics or bleeding edge companies any more
- Come up with the examples of what each group should be doing
- There are three kinds of data problems that really push the big data envelope. They’re commonly known as the Vs of Big Data. Most people agree on at least 3 of them because they help us categorize the technical problem, while others tend to be dimensions of the solution or business problem.
- The value is to decouple the storage from the process
- There are three kinds of data problems that really push the big data envelope. They’re commonly known as the Vs of Big Data. Most people agree on at least 3 of them because they help us categorize the technical problem, while others tend to be dimensions of the solution or business problem.
- Pariveda Solutions has developed a Big Data/AWS reference architecture referred to as the “analytics pipeline” Expands on the familiar [ Extract ] [ Transform ] [ Load ] pattern Terminology-wise, this maps directly to the Analytics Pipeline Extract = Ingest/Collect Transform = Store (Model)/Process (Enhance/Transform) Load = Consume/Visualize (Distribute)
- Pariveda Solutions has developed a Big Data/AWS reference architecture referred to as the “analytics pipeline” Expands on the familiar [ Extract ] [ Transform ] [ Load ] pattern Terminology-wise, this maps directly to the Analytics Pipeline Extract = Ingest/Collect Transform = Store (Model)/Process (Enhance/Transform) Load = Consume/Visualize (Distribute)
- Pariveda Solutions has developed a Big Data/AWS reference architecture referred to as the “analytics pipeline” Expands on the familiar [ Extract ] [ Transform ] [ Load ] pattern Terminology-wise, this maps directly to the Analytics Pipeline Extract = Ingest/Collect Transform = Store (Model)/Process (Enhance/Transform) Load = Consume/Visualize (Distribute)
- Pariveda Solutions has developed a Big Data/AWS reference architecture referred to as the “analytics pipeline” Expands on the familiar [ Extract ] [ Transform ] [ Load ] pattern Terminology-wise, this maps directly to the Analytics Pipeline Extract = Ingest/Collect Transform = Store (Model)/Process (Enhance/Transform) Load = Consume/Visualize (Distribute)
- Pariveda Solutions has developed a Big Data/AWS reference architecture referred to as the “analytics pipeline” Expands on the familiar [ Extract ] [ Transform ] [ Load ] pattern Terminology-wise, this maps directly to the Analytics Pipeline Extract = Ingest/Collect Transform = Store (Model)/Process (Enhance/Transform) Load = Consume/Visualize (Distribute)
- The Data Science work provides insight and value, but how do operationalize the work. This is the challenge. We haven’t necessarily completely figured out how the data science work is part of the agile software development process
- More Info: Jon Landers, Ryan Gross One thing we didn’t cover in the prior section is how to get your models into production Most of the marketecture around Machine Learning will tell you that it’s this easy to deploy your first model as a RESTful service! But then you remember that you’re modeling the real world, and you’ve read some HBR articles about how the real world changes fast, so you’ll need to retrain the model to keep up So you build an API to trigger retraining and validation, and trigger it on a timer Then you build automated deployment of the new model and everyone is happy, the marketecture was right!
- More Info: Jon Landers, Ryan Gross One thing we didn’t cover in the prior section is how to get your models into production Most of the marketecture around Machine Learning will tell you that it’s this easy to deploy your first model as a RESTful service! But then you remember that you’re modeling the real world, and you’ve read some HBR articles about how the real world changes fast, so you’ll need to retrain the model to keep up So you build an API to trigger retraining and validation, and trigger it on a timer Then you build automated deployment of the new model and everyone is happy, the marketecture was right!
- More Info: Jon Landers, Ryan Gross One thing we didn’t cover in the prior section is how to get your models into production Most of the marketecture around Machine Learning will tell you that it’s this easy to deploy your first model as a RESTful service! But then you remember that you’re modeling the real world, and you’ve read some HBR articles about how the real world changes fast, so you’ll need to retrain the model to keep up So you build an API to trigger retraining and validation, and trigger it on a timer Then you build automated deployment of the new model and everyone is happy, the marketecture was right!
- More Info: Jon Landers, Ryan Gross One thing we didn’t cover in the prior section is how to get your models into production Most of the marketecture around Machine Learning will tell you that it’s this easy to deploy your first model as a RESTful service! But then you remember that you’re modeling the real world, and you’ve read some HBR articles about how the real world changes fast, so you’ll need to retrain the model to keep up So you build an API to trigger retraining and validation, and trigger it on a timer Then you build automated deployment of the new model and everyone is happy, the marketecture was right!
- More Info: Jon Landers, Ryan Gross One thing we didn’t cover in the prior section is how to get your models into production Most of the marketecture around Machine Learning will tell you that it’s this easy to deploy your first model as a RESTful service! But then you remember that you’re modeling the real world, and you’ve read some HBR articles about how the real world changes fast, so you’ll need to retrain the model to keep up So you build an API to trigger retraining and validation, and trigger it on a timer Then you build automated deployment of the new model and everyone is happy, the marketecture was right!
- More Info: Ryan Gross Let’s go back to your prediction pipeline When the next transaction, day, week, or month goes by, we can check our predictions against the actual values, detecting the need to re-train our models using the new actuals. If the prediction is off by too much, we can alert the team so they can figure out why. If (as is common), it’s just drift because the real world changed, we can re-train the model
- More Info: Ryan Gross Let’s go back to your prediction pipeline When the next transaction, day, week, or month goes by, we can check our predictions against the actual values, detecting the need to re-train our models using the new actuals. If the prediction is off by too much, we can alert the team so they can figure out why. If (as is common), it’s just drift because the real world changed, we can re-train the model
- More Info: Ryan Gross Let’s go back to your prediction pipeline When the next transaction, day, week, or month goes by, we can check our predictions against the actual values, detecting the need to re-train our models using the new actuals. If the prediction is off by too much, we can alert the team so they can figure out why. If (as is common), it’s just drift because the real world changed, we can re-train the model