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![Prepare Your Data
Text Featurizer
Featurized Text
[0.76, 0.65, 0.44, ā¦]
[0.98, 0.43, 0.54, ā¦]
[0.35, 0.73, 0.46, ā¦]
[0.39, 0, 0.75, ā¦]
Example
Text
==RUDE== Dude, you are rude ā¦
== OK! == IM GOING TO VANDALIZE ā¦
I also found use of the word "humanistsā ā¦
Oooooh thank you Mr. DietLime ā¦
Important concepts: Transformer](https://image.slidesharecdn.com/20210223mvpfusionml-210224144539/75/2021-02-23-MVP-Fusion-Getting-Started-with-Machine-Learning-Net-and-AutoML-9-2048.jpg)
Uploaded byBruno Capuano
2021 02 23 MVP Fusion Getting Started with Machine Learning.Net and AutoML
The document introduces ML.NET, a machine learning framework designed for .NET developers, highlighting its capabilities in various scenarios including sentiment analysis, anomaly detection, and automating machine learning workflows. It provides a step-by-step guide to preparing data, training models, and evaluating results, aiming to simplify the process of machine learning. Additionally, it promotes resources and tools available for users to accelerate model development and enhance productivity.
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2021 02 23 MVP Fusion Getting Started with Machine Learning.Net and AutoML
- 1. Getting started withML.Net and AutoML Bruno Capuano Innovation Lead @Avanade @elbruno | http://elbruno.com
- 2. DESKTOP CLOUD WEB MOBILEML .NET IoT GAMING Your platform for building anything
- 3. Proven & Extensible OpenSource & Cross platform dot.net/ml Build your own Developer Focused ML.NET is a machine learning framework made for .NET developers
- 4. 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
- 5. Load Data Extract Features Model Consumption Train Model Evaluate Model Prepare YourData Build & Train Run Machine Leaning workflow
- 6. Machine Learning.Net Getting Startedwith Sentiment Analysis
- 7. Comment Toxic? (Sentiment) ==RUDE==Dude, you are rude ā¦ 1 == OK! == IM GOING TO VANDALIZE ā¦ 1 I also found use of the word "humanistsā confusing ā¦ 0 Oooooh thank you Mr. DietLime ā¦ 0 Wikipedia detox data at https://figshare.com/articles/Wikipedia_Talk_Labels_Personal_Attacks/4054689 Features (input) Label (output) Sentiment Analysis
- 8. Prepare Your Data Example CommentToxic? (Sentiment) ==RUDE== Dude, you are rude ā¦ 1 == OK! == IM GOING TO VANDALIZE ā¦ 1 I also found use of the word "humanistsā confusing ā¦ 0 Oooooh thank you Mr. DietLime ā¦ 0 Important concepts: Data
- 9. Prepare Your Data TextFeaturizer Featurized Text [0.76, 0.65, 0.44, ā¦] [0.98, 0.43, 0.54, ā¦] [0.35, 0.73, 0.46, ā¦] [0.39, 0, 0.75, ā¦] Example Text ==RUDE== Dude, you are rude ā¦ == OK! == IM GOING TO VANDALIZE ā¦ I also found use of the word "humanistsā ā¦ Oooooh thank you Mr. DietLime ā¦ Important concepts: Transformer
- 10. Build & Train Example Estimator CommentToxic? (Sentiment) ==RUDE== Dude, you ā¦ 1 == OK! == IM GOING ā¦ 1 I also found use of the ā¦ 0 Oooooh thank you Mr. ā¦ 0 Important concepts: Estimator
- 11. Comment ==RUDE== Dude, youā¦ Prediction Function Predicted Label ā Toxic? (Sentiment) 1 Run Example Important concepts: Prediction Function
- 12.
- 13.
- 14. 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.
- 15.
- 17. Load Data Extract Features Model Consumption Train Model Evaluate Model Prepare YourData Build & Train Run Machine Leaning workflow
- 18. Machine Learning.Net AutoML andModel Builder
- 19. AutoML Model Builder ML.NET Tooling ML.NET CLIglobal tool accelerates productivity
- 20. How much isthe taxi fare for 1 passenger going from Burlington to Toronto? ML.NET CLI global tool accelerates productivity AutoML with ML.NET
- 21. Criterion Loss Min Samples Split MinSamples Leaf XYZ Parameter 1 Parameter 2 Parameter 3 Parameter 4 ā¦ Distance Trip time Car type Passengers Time of day ā¦ Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM ā¦ 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. 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 ā¦ Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM ā¦ Nearest Neighbors Model Iterate Gradient Boosted Distance 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. 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
- 24. 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
- 25. 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
- 26.
- 27.
- 28. ..DataSamplesSentimentAnalysiswikiDetoxAnnotated40kRows.tsv | Trainer MicroAccuracyMacroAccuracy Duration #Iteration | |1 AveragedPerceptronOva 0.9518 0.8122 17.1 1 | |2 SdcaMaximumEntropyMulti 0.9530 0.8056 16.3 2 | |3 LightGbmMulti 0.9494 0.8036 83.6 3 | |4 SymbolicSgdLogisticRegressionOva 0.9516 0.8192 17.4 4 | |5 FastTreeOva 0.9477 0.7871 163.8 5 | |6 LinearSvmOva 0.9530 0.8355 13.6 6 | |7 LbfgsLogisticRegressionOva 0.9533 0.8171 39.8 7 | |8 SgdCalibratedOva 0.9491 0.7900 18.2 8 | |9 FastForestOva 0.9362 0.7312 136.0 9 | |10 LbfgsMaximumEntropyMulti 0.9560 0.8289 38.1 10 | ===============================================Experiment Results================================================= ------------------------------------------------------------------------------------------------------------------ | Summary | ------------------------------------------------------------------------------------------------------------------ |ML Task: multiclass-classification | |Dataset: ..DataSamplesSentimentAnalysiswikiDetoxAnnotated40kRows.tsv| Results
- 29. ===============================================Experiment Results================================================= ------------------------------------------------------------------------------------------------------------------ | Summary| ------------------------------------------------------------------------------------------------------------------ |ML Task: multiclass-classification | |Dataset: ..DataSamplesSentimentAnalysiswikiDetoxAnnotated40kRows.tsv| |Label : Label | |Total experiment time : 543.8340995 Secs | |Total number of models explored: 10 | ------------------------------------------------------------------------------------------------------------------ | Top 5 models explored | ------------------------------------------------------------------------------------------------------------------ | Trainer MicroAccuracy MacroAccuracy Duration #Iteration | |1 LbfgsMaximumEntropyMulti 0.9560 0.8289 38.1 1 | |2 LbfgsLogisticRegressionOva 0.9533 0.8171 39.8 2 | |3 SdcaMaximumEntropyMulti 0.9530 0.8056 16.3 3 | |4 LinearSvmOva 0.9530 0.8355 13.6 4 | |5 AveragedPerceptronOva 0.9518 0.8122 17.1 5 | ------------------------------------------------------------------------------------------------------------------ Results
- 30. AutoML ā ImageClassification
- 31. AutoML ā ImageClassification
- 32.
- 33. Getting started withML.Net Bruno Capuano Innovation Lead @Avanade @elbruno | http://elbruno.com
Editor's Notes
- #3Ā .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.
- #4Ā 3
- #5Ā 4
- #6Ā 5
- #8Ā 7
- #9Ā 8
- #10Ā 9
- #11Ā 10
- #12Ā 11
- #15Ā TheĀ Anomaly Detection APIĀ can detect the following types of anomalies on time series data: Spikes and Dips:Ā For example, when monitoring the number of login failures to a service or number of checkouts in an e-commerce site, unusual spikes or dips could indicate security attacks or service disruptions. Positive and negative trends:Ā When monitoring memory usage in computing, for instance, shrinking free memory size is indicative of a potential memory leak; when monitoring service queue length, a persistent upward trend may indicate an underlying software issue. Level changes and changes in dynamic range of values:Ā For example, level changes in latencies of a service after a service upgrade or lower levels of exceptions after upgrade can be interesting to monitor. The machine learning based API enables: Flexible and robust detection:Ā The anomaly detection models allow users to configure sensitivity settings and detect anomalies among seasonal and non-seasonal data sets. Users can adjust the anomaly detection model to make the detection API less or more sensitive according to their needs. This would mean detecting the less or more visible anomalies in data with and without seasonal patterns.Ā Scalable and timely detection:Ā The traditional way of monitoring with preset thresholds set by experts' domain knowledge are costly and not scalable to millions of dynamically changing data sets. The anomaly detection models in this API are learned and models are tuned automatically from both historical and real-time data. Proactive and actionable detection:Ā Slow trend and level change detection can be applied for early anomaly detection. The early abnormal signals detected can be used to direct humans to investigate and act on the problem areas.Ā Ā In addition, root cause analysis models and alerting tools can be developed on top of this anomaly detection API service.Ā The anomaly detection API is an effective and efficient solution for a wide range of scenarios like service health & KPI monitoring, IoT, performance monitoring, and network traffic monitoring. Here are some popular scenarios where this API can be useful: Ā IT departments need tools to track events, error code, usage log, and performance (CPU, Memory and so on) in a timely manner. Online commerce sites wants to track customer activities, page views, clicks, and so on. Utility companies want to track consumption of water, gas, electricity and other resources. Facility/Building management services want to monitor temperature, moisture, traffic and so on. IoT/manufacturers want to use sensor data in time series to monitor work flow, quality and so on. Service providers, such as call centers need to monitor service demand trend, incident volume, wait queue length and so on. Business analytics groups want to monitor business KPIs' (such as sales volume, customer sentiments, pricing) abnormal movement in real time.
- #18Ā 17
- #20Ā 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.
- #21Ā To demonstrate what AutoML is, letās 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?
- #22Ā A data scientistās job is to find the best algorithm that will do taxi fare prediction. Letās 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.
- #23Ā Microsoft Envision 2016
- #24Ā Microsoft Envision 2016
- #25Ā Microsoft Envision 2016
- #26Ā 25