Why you shouldn't probably care about Machine Learning
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SharePoint Saturday Belgium 2018 session by Edin Kapic
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Why you shouldn't probably care about Machine Learning
- 1. Why You Should (Not) Care About Machine Learning Edin Kapić | @ekapic SharePoint Saturday Belgium 2018 #SPSBE
- 3. Patrick Tisseghem Founding father of BIWUG & true SharePoint master 17/10/1968 - 3/9/2008 Special Tribute Edition #SPSBE
- 4. Edin Kapić • SharePoint Master of Craft @ isolutions Barcelona • Cofounder and president of SUG.CAT • MVP for Office Apps and Services • Organizer for SPS Barcelona (October 27th) @ekapic
- 10. Under the hood
- 11. The Roadmap What Why How But
- 12. What Why How But
- 13. Machine Learning
- 14. Machine Learning • Automatically looking for patterns in the data
- 15. Machine Learning • Automatically looking for patterns in the data
- 16. Machine Learning
- 17. Machine Learning • Automatically looking for patterns in the data Alice Foo Baz Bar Bob Doo
- 18. Machine Learning vs Programming Programming Machine Learning RESULTS RULES
- 19. Types of Machine Learning All examples are from https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-choice Clustering (what groups of customers I have?) Recommendation (what song should I listen to?) Regression (how many sales am I making?) Classification (what is in this picture?) Anomaly detection (is this account activity normal?) SUPERVISED UNSUPERVISED
- 20. Regression • Tries to fit observed values into a function curve • Predicts a value (price, size, number of customers…)
- 21. Classification • Splits the decision space into discrete categories and predicts them
- 22. Anomaly Detection • Special kind of classification that highlights outlier values regarding training data
- 23. Clustering • Group observed data into “chunks”
- 24. Recommendation • Associate input and output values to predict by similarity • Mix of classification and reinforcement graphs
- 25. Confused by now? • Azure ML Algorithm Cheat Sheet • Azure ML Basics Infographic with Examples bcned.in/AzureMLCheatsheet bcned.in/AzureMLInfographic
- 26. What Why How But
- 30. React to changes in real time
- 33. What Why How But
- 36. Self-service Machine Learning Models • Pre-trained models • API as a service
- 37. Self-service Machine Learning Models
- 38. SHAREPOINT PICTURE LIBRARY KEYWORD EXTRACTION
- 39. Microsoft ML Studio Amazon SageMaker
- 40. Azure ML Studio / Azure ML Experimentation Service • Platform for ML models and experiments
- 41. Amazon SageMaker
- 45. CNTK: Cognitive Toolkit • Open-source deep learning toolkit by Microsoft https://www.microsoft.com/en-us/cognitive-toolkit/
- 46. Windows ML • Evaluates pre-trained models on Windows platform https://docs.microsoft.com/en-us/windows/uwp/machine-learning/
- 47. TensorFlow • Open-source machine learning framework https://www.tensorflow.org/
- 48. ONNX: Model Interchange Format • Open Neural Network Exchange Format • Developed by Microsoft, Facebook and Amazon • Gallery of models at https://github.com/onnx/models
- 50. What Why How But
- 52. Limitations of Machine Learning
- 53. ML Limitations
- 54. Ethical Concerns
- 55. Privacy Concerns
- 57. What • Pattern recognition • Supervised • Unsupervised Why • Prediction • Realtime • Time-consuming How • Services • Platforms • Libraries But • Ethics • Privacy • Limitations Machine Learning
- 58. #SPSBE http://spsbe.be Please rate this session!
- 59. SharePoint Saturday Belgium 2018 #SPSBE
Notas do Editor
- Predict some future behaviour Tell me what my factory workload is going to be next month.
- Quickly do time-consuming classification Here are 100.000 animal photos. Please classify them into dogs, cats and parrots.
- React to changes in real time I want the turbine blades on the airplane changed as soon as they show signs of wear.
- Perform operations that are non-scriptable Here is the driving wheel, Tesla. Drive according to the traffic.
- Metadata extraction Autoclassification Summarizing Recommendations Relating data
- https://transcranial.github.io/keras-js/#/inception-v3 Normal cat: https://i.imgur.com/ZJR3S7F.jpg Hacked cat: https://i.imgur.com/vTKgnFR.png
- Correlation is not causation
- Uncertainty is always there
- What biases are there in the source data? What information is used as an estimator? Is machine doing a human-relevant decision?
- Big Data and user-generated data Ownership of data Transparency of models
- Not a silver bullet Can be hacked