reference point for "Advanced Developers Conference 21" attendees - ml4net.ppt https://github.com/gldnblgty/ADC21_HandsOnDemo

1. Machine Learning for .NET Developers
Gülden Bilgütay
@gubilgu

2. A few points about ML concepts to know
as a .NET developer…

3. Machine Learning
Typical E2E Process
…
Prepare Experiment Deploy
Orchestrate

4. Complexity of Machine Learning
Source: http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
- Mean Absolute Error (MAE)
- Root of mean squared Error (RMSE)
- Area under the curve (AUC)
- Loss function
- Log loss
- Accuracy
- Precision
- Recall
- F-score
- Hyperparameters
- # of nodes
- # of trees
- Weights of nodes …etc.
….
……
………
Evaluation Metrics

7. AutoML
1.Identify the ML problem to be solved: classification, forecasting, regression…etc.
2.Specify the source and format of the labeled training data:
3.Configure the compute target for model training
4.Configure the automated machine learning parameters
5.Submit the training run.
6.Review the results

8.  Open-source proxy between .NET and ML community
 Automatic predictions using the data available to your application

9. Who is using ML.NET?
 At Microsoft:
 Outlook, M365, O365, Microsoft Defender, Azure AutoML, Power BI...
 For other real use cases, check out:
ML.NET Customer Showcase
ML.NET in the Real World @ .NET Conf

10. Resources
http://dot.net/ml
http://aka.ms/mlnetsamples
http://aka.ms/mlnetdocs
http://aka.ms/mlnet
https://aka.ms/mlnetyoutube
http://aka.ms/mlnet-workshop

11. • Collect and load training data into an IDataView object
• Specify a pipeline of operations to extract features and apply a
machine learning algorithm
• Train a model by calling Fit() on the pipeline
• Evaluate the model and iterate to improve
• Save the model into binary format, for use in an application
• Load the model back into an ITransformer object
• Make predictions by calling CreatePredictionEngine.Predict()

12. Model Portability - ONNX
Use your favorite deep learning frameworks without getting locked into one framework
ONNX
Community project created by Facebook and Microsoft
Use the best tool for the job. Train in one framework and
transfer to another for inference
TensorFlow PyTorch Scikit-Learn
MXNet Chainer Keras

13. Let’s Demo - Hands-On

15. PROs & CONs

16. E2E scenarios

17. Edge deployment

18. https://docs.microsoft.com/en-
us/azure/architecture/reference-
architectures/ai/end-to-end-smart-factory

19. https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/mlops-python

20. © Copyright Microsoft Corporation. All rights reserved.
Q&A – Thank you !
https://github.com/gldnblgty/ADC21_HandsOnDemo