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DutchMLSchool. ML for Energy Trading and Automotive Sector

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Machine Learning for Energy Trading, Automotive Sector, and Logistics, presented by BigML's Partners A1 Digital.
Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.

Published in: Data & Analytics
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DutchMLSchool. ML for Energy Trading and Automotive Sector

  1. 1. Machine Learning for Energy Trading, Automotive Sector and Logistics BigML Summerschool, Breukelen, NL Dr. Dieter Mayr, 8th of July, 2019
  2. 2. 2 i. A1 Digital Machine Learning Platform powered by BigML ii. Our perception on Machine Learning in various industries iii. Use cases and solutions iv. Our approach and recommendations Agenda
  3. 3. 3 Who we are A1 Digital – IoT, ML, Cloud and Security Focus 3 Headquarters Vienna, Munich and Lausanne - present in 10 countries 180 Employees More than 500 international customer projects AffiliatedGroup A1 Telekom Austria Group & America Móvil Your Partner for Cloud, ML, IoT and Security
  4. 4. 4 A1 Digital‘s ML Team  Our Mission: A1 Digital enables its customers to perform Machine Learning and Advanced Analytics at Scale  Core ML Team: Data Scientists as … ML Consultants … Product Manager … Data Engineer & DevOps  Our technology stack: one large BigML deployment running in our own cloud (Exoscale)  Our offer: Ml
  5. 5. Our perception on Machine Learning in various industries
  6. 6. 6
  7. 7. 7 Market starts demanding ML How usually discover B2B customers dealing with ML: SMEs  Little strategy on ML, mainly individual persons  Little low-hanging fruits: they know how to solve their problems  Depending on industry: IT mostly drives efforts for ML  Input often from the higher management „to try ML“  Many obstacles: little resources (time), fear of learning curve, underestimation of potential of own data, bad first experiences Large Enterprises  Centralized vs decentralized ML Teams  Often: strategy + „center of excellence“  Large investments in complex infrastructure  Communication: Business vs. Data Science team  Lack of agile and simple ways to explore ML
  8. 8. Machine Learning requires a team!
  9. 9. 9 The playing field in ML is moving very fast
  10. 10. 10 Data related project remain complex…. • Even with best tools, it remains a challenge to find a use case with adequate data… • ML is just one (important!) element but cannot solve all problems • But: with ML project, stakeholders learn, understand the imporance of data and become creative in finding new use cases in thair fields.
  11. 11. 11 But where we believe BigML is essential creating added value • Nothing to install or configure • No programming skills required • Smart data input • Automatic Modeling • Smart model consumption PROGRAMMABLE • Offer basic constructors that enable sophisticated ML strategies • API-first SCALABLE • Fully-automated infrastructure • Instant access, instant scale • All the complexities related to infrastructure are abstracted away • Serverless
  12. 12. Use Cases and Solutions
  13. 13. 13 What kind of ML Platform is needed ? • Nothing to install or configure • No programming skills required • Smart data input • Automatic Modeling • Smart model consumption PROGRAMMABLE • Offer basic constructors that enable sophisticated ML strategies • API-first SCALABLE • Fully-automated infrastructure • Instant access, instant scale • All the complexities related to infrastructure are abstracted away • Serverless
  14. 14. 14 Machine Learning for Energy Trading REQUIREMENTS  Automated workflow for predicting control energy prices  Evaluation of historic bids and revenues OUR SOLUTION  Analysis of historic trading strategy  Expert workshops for defining data sources (spot market prices, weather, etc.) and feature engineering  Identify best performing machine learning algorithms  Dashboard as decision support tool for trading and evaluating historic bids RESULTS  10 % higher revenues from auctions  More transparent decisions and reduced workload
  15. 15. 15 Energy Trading Dashboard ML Application for Energy Trading Download data & Feature Engineering Auction announcements Filter announcements „ML-ready Data“ Price prediction for the next auction Auction results Additional data
  16. 16. 16 What kind of ML Platform is needed ? • Nothing to install or configure • No programming skills required • Smart data input • Automatic Modeling • Smart model consumption PROGRAMMABLE • Offer basic constructors that enable sophisticated ML strategies • API-first SCALABLE • Fully-automated infrastructure • Instant access, instant scale • All the complexities related to infrastructure are abstracted away • Serverless
  17. 17. 17 Machine Learning for Wagon Hire and Rail Logistics REQUIREMENTS  Forecast model on maintenance time required for each wagon  Creation of complex data model  Scalable Solution to rapidly analyze vast amounts of data  Open, exportable models, ready to use in production OUR SOLUTION  Consulting on use case selection  Workshop on data selection (gain vs. efforts)  Review of BigML platform how it fits into their requirements  Test OptiML on existing challenges RESULTS  OptiML outperforms existing models  Data Science unit works faster, increases collaboration  Leveraging existing models & efforts (from Python)
  18. 18. 18 Bindings How to use bindings for the ML platform? Python example Add source file Create a dataset from source Split data set into training and test datasets Create a model (decision tree) with a training dataset Evaluate with a test dataset Get desired evaluation parameters
  19. 19. 19 Bindings Use the dashboard to evaluate any steps Look up the Script-ID Create execution for new source with Script-ID “Scriptify” one step or the whole workflow
  20. 20. 20 What kind of ML Platform is needed ? • Nothing to install or configure • No programming skills required • Smart data input • Automatic Modeling • Smart model consumption PROGRAMMABLE • Offer basic constructors that enable sophisticated ML strategies • API-first SCALABLE • Fully-automated infrastructure • Instant access, instant scale • All the complexities related to infrastructure are abstracted away • Serverless
  21. 21. 21 Machine Learning for Automotive Supplier REQUIREMENTS  Central ML Platform enabling potentially thousands of engineers worldwide to get started with ML  Easy entry in ML with ability to fully scale fast OUR SOLUTION  Discuss ML efforts and strategy  Develop a PoC (on injection molding machines)  Evaluate results and feedback from subject matter experts  Consulting to create a concept about how to roll- out ML to business units RESULTS  10+ potential use cases  Roadmap, learning program and show cases  Cost-effective strategy for a global accessable ML platform enabling engineers to optimize data related routines
  22. 22. 22 Challenging market expectations demands development of ML-led Sense-Predict-React capabilities • Conformity to specification • Product performance Quality • Low Rework cost • High percentage of passed quality inspection • Low cost of quality control • Delivery Lead Times • On Time Delivery • Stock availability Delivery • Short production and delivery lead time • High accuracy of inventory status • High dependability of internal lead times • Product Selling • Competitive Pricing • Disruption driver Cost • Low unit cost of manufacturing • Fast inventory turnover • High capacity utilization • Product range • Product portfolio offered • Volume / product mix changes Flexibility • Shortest MRP and set up times • Shortest length of fixed production schedule • Optimal amount of operating capacity
  23. 23. Our approach and recommendations
  24. 24. DataExploration • Use-Case workshop • First, quick results • Follow-up potential Pilot Package • Multiple workshops • Multiple data sources • Reliable results ProductiveSystem • Improve pilot solution • Integration in existing system. • Deployment of Application Expandon yourown • Implement further use cases on your own The way to Machine Learning based applications We enable our customers step-by-step Continuous Machine Learning trainings and support by A1 Digital experts from data to results in about 6 weeks
  25. 25. 25  Prioritize  impact & reuse  Develop ML strategy  core analytical capabilities & easy access (MLaaS) platforms  Leverage partner  stay focused on your business  Educate (repeatedly)  Unleash the Citizen Data Scientist.  Domain knowledge  respect expertise and bring ML closer to decision making  Freedom  Allow freedom for creativity and potentially failing  Small steps  focus on fast first projects and stay agile  Start now Recommendations to our customers
  26. 26. Thank you for you attention contact us: dieter.mayr@a1.digital

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