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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/20200229techdayconf03-slideshare-200301233435/75/2020-02-29-TechDay-Conf-Getting-started-with-Machine-Learning-Net-15-2048.jpg)
Uploaded byBruno Capuano
2020 02 29 TechDay Conf - Getting started with Machine Learning.Net
The document provides an overview of ML.NET, a machine learning framework aimed at .NET developers, highlighting its capabilities such as sentiment analysis, customer segmentation, and predictive maintenance. It emphasizes the ease of use and flexibility of the platform, as well as the integration with Azure Cognitive Services for various machine learning tasks. Additionally, it discusses tools like AutoML and Model Builder that accelerate productivity in machine learning workflows.
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2020 02 29 TechDay Conf - Getting started with Machine Learning.Net
- 1. Getting started withML.Net Bruno Capuano Innovation Lead @Avanade @elbruno | http://elbruno.com
- 2. @elbruno Excessive use of crappy animations anddemos! https://wallup.net/dark-horror-evil-sign-text-zombie/
- 3. @elbruno DESKTOP CLOUDWEB MOBILEML .NET IoTGAMING Your platform for building anything
- 4. @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
- 5. @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
- 6. @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
- 7. @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
- 8. @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
- 9. @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
- 10. @elbruno Load Data Extract Features Model Consumption Train Model Evaluate Model Prepare YourData Build & Train Run Machine Leaning workflow
- 11. Machine Learning.Net Getting Startedwith Sentiment Analysis
- 12. @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
- 13. @elbruno Is this Aor B? Is this a toxic comment? Yes or no Sentiment analysis explained
- 14. @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
- 15. @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
- 16. @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
- 17. @elbruno Comment ==RUDE== Dude, you⌠Prediction Function Predicted Label â Toxic? (Sentiment) 1 Run Example Important concepts: Prediction Function
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- 19. Machine Learning.Net AutoML andModel Builder
- 20. @elbruno AutoML Model Builder ML.NET Tooling ML.NET CLIglobal tool accelerates productivity
- 21.
- 22. @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
- 23. @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
- 24. @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
- 25. @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
- 26. @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
- 27. @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
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- 30. @elbruno Bruno Capuano Innovation Lead@Avanade @elbruno | http://elbruno.com Q&A Thanks!
Editor's Notes
- #4Â .NET is a great tech stack for building a wide variety of applications. There is ASP.NET for web development, Xamarin for mobile development and with ML.NET we are trying to make .NET great for Machine Learning.
- #5Â Even though we just recently released ML.NET at Build this year, ML.NET has been used at Microsoft heavily for over a decade by iconic MS products. Bing Ads uses ML.NET for add-click predictions Excel uses ML.NET for chart recommendations PowerPoint uses ML.NET for Design Ideas Windows10 uses ML.NET for Windows Defender
- #6Â 5
- #7Â 6
- #8Â 7
- #9Â 8
- #10Â 9
- #11Â 10
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- #15Â 14
- #16Â 15
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- #18Â 17
- #21Â ML.NET provides tooling that makes it easy to use. In particular, 2 really valuable tools are: AutoML and Model Builder What is AutoML? It is an API that accelerates model development for you. A lot of developers do not have the experience required to build or train Machine Learning models. With AutoML, the process of finding the best algorithm, is automated! Model Builder on the other hand provides an easy to understand visual interface to build, train, and deploy custom machine learning models. Prior machine learning expertise is not required. It also supports AutoML Rememeber depending on your data, giving you the error of each of the models and you can then decide which model to use. Most people just use the model with the least error. And we will see it in action soon.
- #23Â To demonstrate what AutoML is, letâs consider that we want to provide a service that allows users to predict taxi fare before they book or call a taxi. How can we build this feature/service?
- #24Â A data scientistâs job is to find the best algorithm that will do taxi fare prediction. Letâs says we have a dataset that contains information such as trip distance, trip time, number of passengers, time of day of the trip etc. A data scientist will spend a lot of time trying to decide which of these pieces of information is important when predicting taxi fare. In ML, there are so many algorithms and are generally referred to as trainers, for example linear regression, convolutional neural network etc The data scientist will try one algorithm at a time, picking features as he desires, and then wait to see how the model performs. In this case, this model only scored 30% based on number of bad predictions it made.
- #25Â Microsoft Envision 2016
- #26Â Microsoft Envision 2016
- #27Â Microsoft Envision 2016
- #28Â 27