2019 12 19 Mississauga .Net User Group - Machine Learning.Net and Auto ML
Download as PPTX, PDF2 likes322 views
The document discusses ML.NET, a machine learning framework designed for .NET developers, highlighting its features, such as support for a variety of machine learning scenarios like sentiment analysis, customer segmentation, and predictive maintenance. It emphasizes the ease of use and productivity enhancements provided by the AutoML capabilities within ML.NET, enabling users to streamline data preparation, feature selection, and hyperparameter tuning. Furthermore, the document includes code examples and resources for getting started with ML.NET and using its tools effectively.
![@elbruno
Prepare Your Data
Text Featurizer
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](https://image.slidesharecdn.com/20191219mississauga-191220004627/85/2019-12-19-Mississauga-Net-User-Group-Machine-Learning-Net-and-Auto-ML-14-320.jpg)
2019 12 19 Mississauga .Net User Group - Machine Learning.Net and Auto ML
- 1. Machine Learning.Net and Auto ML Bruno Capuano Innovation Lead @Avanade @elbruno | http://elbruno.com
- 2. @elbruno DESKTOP CLOUDWEB MOBILE ML .NET IoTGAMING Your platform for building anything
- 3. @elbruno Windows 10 (Windows Defender) 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
- 4. @elbruno Proven & Extensible Open Source & Cross platform dot.net/ml Build your own Developer Focused ML.NET is a machine learning framework made for .NET developers
- 5. @elbruno And many more examples @ 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
- 6. @elbruno Easy / Less Control 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
- 7. @elbruno Easy / Less Control 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
- 8. @elbruno Easy / Less Control 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
- 9. @elbruno Load Data Extract Features Model Consumption Train Model Evaluate Model Prepare Your Data Build & Train Run Machine Leaning workflow
- 10. Global AI Night – Machine Learning.Net Getting Started with Sentiment Analysis
- 11. @elbruno 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
- 12. @elbruno Is this A or B? Is this a toxic comment? Yes or no Sentiment analysis explained
- 13. @elbruno Prepare Your Data Example 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 Important concepts: Data
- 14. @elbruno Prepare Your Data Text Featurizer 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
- 15. @elbruno Build & Train Example Estimator Comment Toxic? (Sentiment) ==RUDE== Dude, you … 1 == OK! == IM GOING … 1 I also found use of the … 0 Oooooh thank you Mr. … 0 Important concepts: Estimator
- 16. @elbruno Comment ==RUDE== Dude, you … Prediction Function Predicted Label – Toxic? (Sentiment) 1 Run Example Important concepts: Prediction Function
- 18. Global AI Night – Machine Learning.Net AutoML and Model Builder
- 19. @elbruno AutoML Model Builder ML.NET Tooling ML.NET CLI global tool accelerates productivity
- 20. @elbruno How much is the taxi fare for 1 passenger going from Burlington to Toronto? ML.NET CLI global tool accelerates productivity AutoML with ML.NET
- 21. @elbruno Criterion Loss Min Samples Split Min Samples 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
- 22. @elbruno N Neighbors Weights Metric P ZYX Criterion Loss Min Samples Split 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
- 23. @elbruno Which algorithm? Which parameters?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
- 24. @elbruno 25%40%70% 25% 95% 25% 25% 25% 25% 40% 40% 40% 40% 70% 70% 70%Enter data Define goals Apply constraints Input Intelligently test multiple models in parallel Optimized model 95% ML.NET accelerates model development
- 25. @elbruno 70%95% Feature importance Distance Trip time 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
- 26. #insiderDevTour # 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)
- 29. Machine Learning.Net and Auto ML Bruno Capuano Innovation Lead @Avanade @elbruno | http://elbruno.com