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Prepare Your Data
Text Featurizer
Featurized Text
[0.76, 0.65, 0.44, âŚ]
[0.98, 0.43, 0.54, âŚ]
[0.35, 0.73, 0.46, âŚ]
[0.39, 0, 0.75, âŚ]
Example
Text
==RUDE== Dude, you are rude âŚ
== OK! == IM GOING TO VANDALIZE âŚ
I also found use of the word "humanistsâ âŚ
Oooooh thank you Mr. DietLime âŚ
Important concepts: Transformer](https://image.slidesharecdn.com/20190905globalainightmlnet06-slideshare-190906141107/75/2019-09-05-Global-AI-Night-Toronto-Machine-Learning-Net-18-2048.jpg)
Uploaded byBruno Capuano
2019 09 05 Global AI Night Toronto - Machine Learning.Net
This document contains information about ML.NET, a machine learning framework for .NET developers. It discusses how ML.NET simplifies the machine learning process by taking care of data preparation, feature selection, and hyperparameter tuning. It also describes how ML.NET accelerates model development through automated machine learning (AutoML) and model explainability features. The document provides code examples of using ML.NET for sentiment analysis and AutoML for a taxi fare prediction problem.
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2019 09 05 Global AI Night Toronto - Machine Learning.Net
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- 4. Bruno Capuano Innovation Lead@Avanade @elbruno | http://elbruno.com Machine Learning.Net
- 5. @elbruno Agent Applications ServicesInfrastructure Microsoft AI Portfolio Cortana Office 365 Dynamics 365 SwiftKey Pix Customer Service and Support Bot Framework Cognitive Services Cortana Intelligence Cognitive Toolkit Azure Machine Learning Azure N Series FPGA People
- 6. @elbrunoŠ2017 Avanade Inc.All Rights Reserved.
- 7. @elbruno DESKTOP CLOUDWEB MOBILEML .NET IoTGAMING Your platform for building anything
- 8. @elbruno Windows 10 (WindowsDefender) Power Point (Design Ideas) Excel (Chart Recommendations) Bing Ads (Ad Predictions) + more Azure Stream Analytics (Anomaly Detection) Power BI (Key Influencers) ML.NET is proven at scale, enterprise ready
- 9. @elbruno Proven & Extensible OpenSource & Cross platform dot.net/ml Build your own Developer Focused ML.NET is a machine learning framework made for .NET developers
- 10. @elbruno And many moreexamples @ https://github.com/dotnet/machinelearning-samples Customer segmentation Recommendations Predictive maintenance Forecasting Issue Classification Ranking news/topics Image classification Sentiment Analysis Machine Learning scenarios with ML.NET
- 11. @elbruno Easy / LessControl Full Control / Harder Vision Speech Language Knowledge SearchLabs TextAnalyticsAPI client = new TextAnalyticsAPI(); client.AzureRegion = AzureRegions.Westus; client.SubscriptionKey = "1bf33391DeadFish"; client.Sentiment( new MultiLanguageBatchInput( new List<MultiLanguageInput>() { new MultiLanguageInput("en","0", "This is a great vacuum cleaner") })); e.g. Sentiment Analysis using Azure Cognitive Services 96% positive Pre-built machine learning models
- 12. @elbruno Easy / LessControl Full Control / Harder TextAnalyticsAPI client = new TextAnalyticsAPI(); client.AzureRegion = AzureRegions.Westus; client.SubscriptionKey = "1bf33391DeadFish"; client.Sentiment( new MultiLanguageBatchInput( new List<MultiLanguageInput>() { new MultiLanguageInput("en","0", "This vacuum cleaner sucks so much dirt") })); e.g. Sentiment Analysis using Azure Cognitive Services 9% positive Vision Speech Language Knowledge SearchLabs Limitations with pre-built machine learning models
- 13. @elbruno Load Data Extract Features Model Consumption Train Model Evaluate Model Prepare YourData Build & Train Run Machine Leaning workflow
- 14. Global AI Nightâ Machine Learning.Net Getting Started with Sentiment Analysis
- 15. @elbruno Comment Toxic? (Sentiment) ==RUDE==Dude, you are rude ⌠1 == OK! == IM GOING TO VANDALIZE ⌠1 I also found use of the word "humanistsâ confusing ⌠0 Oooooh thank you Mr. DietLime ⌠0 Wikipedia detox data at https://figshare.com/articles/Wikipedia_Talk_Labels_Personal_Attacks/4054689 Features (input) Label (output) Sentiment Analysis
- 16. @elbruno Is this Aor B? Is this a toxic comment? Yes or no Sentiment analysis explained
- 17. @elbruno Prepare Your Data Example CommentToxic? (Sentiment) ==RUDE== Dude, you are rude ⌠1 == OK! == IM GOING TO VANDALIZE ⌠1 I also found use of the word "humanistsâ confusing ⌠0 Oooooh thank you Mr. DietLime ⌠0 Important concepts: Data
- 18. @elbruno Prepare Your Data TextFeaturizer Featurized Text [0.76, 0.65, 0.44, âŚ] [0.98, 0.43, 0.54, âŚ] [0.35, 0.73, 0.46, âŚ] [0.39, 0, 0.75, âŚ] Example Text ==RUDE== Dude, you are rude ⌠== OK! == IM GOING TO VANDALIZE ⌠I also found use of the word "humanistsâ ⌠Oooooh thank you Mr. DietLime ⌠Important concepts: Transformer
- 19. @elbruno Build & Train Example Estimator CommentToxic? (Sentiment) ==RUDE== Dude, you ⌠1 == OK! == IM GOING ⌠1 I also found use of the ⌠0 Oooooh thank you Mr. ⌠0 Important concepts: Estimator
- 20. @elbruno Comment ==RUDE== Dude, you⌠Prediction Function Predicted Label â Toxic? (Sentiment) 1 Run Example Important concepts: Prediction Function
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- 22. Global AI Nightâ Machine Learning.Net AutoML and Model Builder
- 23. @elbruno AutoML Model Builder ML.NET Tooling ML.NET CLIglobal tool accelerates productivity
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- 25. @elbruno How much isthe taxi fare for 1 passenger going from Burlington to Toronto? ML.NET CLI global tool accelerates productivity AutoML with ML.NET
- 26. @elbruno Criterion Loss Min Samples Split MinSamples Leaf XYZ Parameter 1 Parameter 2 Parameter 3 Parameter 4 ⌠Distance Trip time Car type Passengers Time of day ⌠Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM ⌠Distance Gradient Boosted Model Car type Passengers Getting started w/machine learning can be hard ML.NET takes the guess work out of data prep, feature selection & hyperparameter tuning Which algorithm? Which parameters?Which features? Getting started w/machine learning can be hard
- 27. @elbruno N Neighbors Weights Metric P ZYX Criterion Loss Min SamplesSplit Min Samples Leaf XYZ Which algorithm? Which parameters?Which features? Distance Trip time Car type Passengers Time of day ⌠Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM ⌠Nearest Neighbors Model Iterate Gradient BoostedDistance Car brand Year of make Car type Passengers Trip time Getting started w/machine learning can be hard ML.NET takes the guess work out of data prep, feature selection & hyperparameter tuning Getting started w/machine learning can be hard
- 28. @elbruno Which algorithm? Whichparameters?Which features? Iterate Getting started w/machine learning can be hard ML.NET takes the guess work out of data prep, feature selection & hyperparameter tuning Getting started w/machine learning can be hard
- 29. @elbruno 25%40%70% 25% 95% 25% 25% 25% 25% 40% 40% 40% 40% 70% 70% 70%Enter data Definegoals Apply constraints Input Intelligently test multiple models in parallel Optimized model 95% ML.NET accelerates model development
- 30. @elbruno 70%95% Feature importance Distance Triptime Car type Passengers Time of day 0 1 Model B (70%) Distance 0 1 Trip time Car type Passengers Time of day Feature importance Model A (95%) ML.NET accelerates model development with model explainability ML.NET accelerates model development
- 31. @elbruno # STEP 1:Load data IDataView trainingDataView = mlContext.Data.LoadFromTextFile<TaxiTrip>( ... ) IDataView testDataView = mlContext.Data.LoadFromTextFile<TaxiTrip>( ... ) ConsoleHelper.ShowDataViewInConsole(mlContext, trainingDataView) # STEP 2: Initialize user-defined progress handler that AutoML will invoke after each model var progressHandler = new RegressionExperimentProgressHandler() # STEP 3: Run AutoML regression experiment ExperimentResult<RegressionMetrics> experimentResult = mlContext.Auto() .CreateRegressionExperiment(ExperimentTime) .Execute(trainingDataView, LabelColumnName, progressHandler: progressHandler) PrintTopModels(experimentResult) # STEP 4: Evaluate the model on test data RunDetail<RegressionMetrics> best = experimentResult.BestRun ITransformer trainedModel = best.Model IDataView predictions = trainedModel.Transform(testDataView) # STEP 5: Save trained model to a .ZIP file mlContext.Model.Save(trainedModel, trainingDataView.Schema, ModelPath)
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- 34. @elbruno Bruno Capuano Innovation Lead@Avanade @elbruno | http://elbruno.com Q&A Thanks!