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![Prepare Your Data
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[0.76, 0.65, 0.44, …]
[0.98, 0.43, 0.54, …]
[0.35, 0.73, 0.46, …]
[0.39, 0, 0.75, …]
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==RUDE== Dude, you are rude …
== OK! == IM GOING TO VANDALIZE …
I also found use of the word "humanists” …
Oooooh thank you Mr. DietLime …
Important concepts: Transformer](https://image.slidesharecdn.com/20200404netcoreconfmlnet01-slideshare-200406135912/85/2020-04-04-NetCoreConf-Machine-Learning-Net-20-320.jpg)
Uploaded byBruno Capuano
2020 04 04 NetCoreConf - Machine Learning.Net
The document introduces ML.NET, a machine learning framework designed for .NET developers, highlighting its enterprise readiness and various applications such as sentiment analysis, anomaly detection, and customer segmentation. It emphasizes the ease of use provided by AutoML and model builder tools for data preparation, feature selection, and hyperparameter tuning, as well as includes code examples and demos for practical implementation. The content encourages users to explore ML.NET through links and resources provided for further learning and application.
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2020 04 04 NetCoreConf - Machine Learning.Net
- 1. 2020 Netcoreconf Getting started withML.Net and AutoML Bruno Capuano Microsoft AI MVP - Innovation Lead @elbruno
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- 3. Excessive use of crappy animationsand demos! https://wallup.net/dark-horror-evil-sign-text-zombie/
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- 6. Deep Neural Network:Cat vs Dog https://becominghuman.ai/building-an-image-classifier-using-deep-learning-in-python-totally-from-a-beginners-perspective-be8dbaf22dd8
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- 8. Why is thishard? You see this: But the camera sees this:
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- 10. DESKTOP CLOUDWEB MOBILEML .NET IoTGAMING Your platform for building anything
- 11. 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
- 12. Proven & Extensible OpenSource & Cross platform dot.net/ml Build your own Developer Focused ML.NET is a machine learning framework made for .NET developers
- 13. 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
- 14. 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
- 15. 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
- 16. Load Data Extract Features Model Consumption Train Model Evaluate Model Prepare YourData Build & Train Run Machine Leaning workflow
- 17. Machine Learning.Net Getting Startedwith Sentiment Analysis
- 18. Comment Toxic? (Sentiment) ==RUDE==Dude, you are rude … 1 == OK! == IM GOING TO VANDALIZE … 1 I also found use of the word "humanists” 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
- 19. 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 "humanists” confusing … 0 Oooooh thank you Mr. DietLime … 0 Important concepts: Data
- 20. Prepare Your Data TextFeaturizer Featurized Text [0.76, 0.65, 0.44, …] [0.98, 0.43, 0.54, …] [0.35, 0.73, 0.46, …] [0.39, 0, 0.75, …] Example Text ==RUDE== Dude, you are rude … == OK! == IM GOING TO VANDALIZE … I also found use of the word "humanists” … Oooooh thank you Mr. DietLime … Important concepts: Transformer
- 21. 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
- 22. Comment ==RUDE== Dude, you… Prediction Function Predicted Label – Toxic? (Sentiment) 1 Run Example Important concepts: Prediction Function
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- 25. Anomaly Detection Anomaly detectiondetects data points in data that does not fit well with the rest of the data. It has a wide range of applications such as fraud detection, surveillance, diagnosis, data cleanup, and predictive maintenance.
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- 27. Load Data Extract Features Model Consumption Train Model Evaluate Model Prepare YourData Build & Train Run Machine Leaning workflow
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- 29. Machine Learning.Net AutoML andModel Builder
- 30. AutoML Model Builder ML.NET Tooling ML.NET CLIglobal tool accelerates productivity
- 31. How much isthe taxi fare for 1 passenger going from Burlington to Toronto? ML.NET CLI global tool accelerates productivity AutoML with ML.NET
- 32. 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
- 33. 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
- 34. 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
- 35. 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
- 36. 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
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- 38. # 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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