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Similar to PyData NYC: Build an AI-powered Pet Detector with Visual Studio Code
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PyData NYC: Build an AI-powered Pet Detector with Visual Studio Code
- 1. Build an AI-powered Pet Detector with Visual Studio Code
- 2. Hello!I’m Katherine Kampf I work on Python AI tools at Microsoft You can find me at @kvkampf And email me at kakampf@microsoft.com
- 3. How do we classify dogs and cats? Alaskan Malamutes vs Siberian Huskies Whippet vs Italian Greyhound
- 4. Deep Learning ~BLACK BOX~ Dog (0.96) Cat (0.03) Other (0.01)
- 5. Deep Learning vs. Machine Learning DL feature learning + classification Cat Dog Other manual feature extraction ML classification algorithm Cat Dog Other
- 6. Choosing a Deep Neural Network ● Convolutional Neural Network (CNN) ○ Great machine learning model for image classification penultimate layer RGB channels of input image Convolution layer Pooling layer Fully connected layer Cat Dog Other
- 7. data exploration training inferencing training script compute tuning model applicationproductization
- 9. • 37 category pet dataset • 200 images for each class • http://www.robots.ox.ac.u k/~vgg/data/pets/ Azure Open Datasets
- 10. ● Many great tools ● Using notebooks for easy setup and quick iteration ● Jupyter support in VS Code ○ Power of VS Code brought to the world of notebooks ○ Intelligent autocompletions ○ Variable explorer ○ Remote development ○ Convert to a Python script
- 13. Deep Learning with small datasets ● By nature, Deep Learning requires ○ Massive amounts of training data ○ Huge computation resources ● ~200 images per breed is not enough ● Possible solutions for working with small datasets: ○ Get more data – larger datasets have huge requirement on compute and time ○ Reuse pre-trained models - Transfer Learning
- 14. ● A popular technique in deep learning that allows you to train Deep Neural Networks with comparatively little data. ● Reuse a pre-trained model on a new, but related, problem. ● Retrain the last fully-connected layer
- 16. Azure Machine Learning service Bring AI to everyone with an end-to-end, scalable, trusted platform Built with your needs in mind Support for open source frameworks Managed compute DevOps for machine learning Simple deployment Automated machine learning Seamlessly integrated with the Azure Portfolio Boost your data science productivity Increase your rate of experimentation Deploy and manage your models everywhere Simplified hyperparameter tuning
- 18. • Import / export Jupyter Notebooks • Visualize data frames and plots • Variable Explorer / Data Viewer • Integrated IPython/Jupyter console • Refactoring • IntelliSense and Debugging • Real-time collaboration with Live Share • Remote development (SSH, containers, WSL)
- 20. • Connect to AML services from within VS Code • Manage workspaces • Submit experiments • Register models • Deploy services • View pipelines Microsoft Confidential
- 21. 1. Explored data and trained model in Jupyter notebooks 2. Used Azure Machine Learning to tune our model 3. Converted our notebook into a Python module 4. Deployed a web service 5. Tested
- 22. ● Build your own pet detector! ○ Pet Detector Github repo: aka.ms/pet-detector-demo ● Try out the VS Code Jupyter support today: ○ aka.ms/vscode-jupyter
- 23. ● Pet Detector Github repo: aka.ms/pet-detector-demo ● Python extension for VS Code: aka.ms/python-extension ● Azure Machine Learning extension for VS Code: aka.ms/aml-extension
- 24. Thanks! Any questions? You can find me at @kvkampf & kakampf@microsoft.com
Editor's Notes
- So, how do we teach machines how to classify dogs? Let’s walkthrough a few things we will need: data source for training, techniques, and the framework to use when it comes to implementation.
- Convolutional neural network (CNN) is the best machine learning model for image classification, but in this case, there are not enough training examples to train it. It would not be able to learn generic enough patterns off this dataset to classify different dog breeds. Most likely, it will just overfit to this small amount of training examples so that accuracy on the test set will be low. There are two possible approaches to mitigate the lack of training examples: Merge dogs dataset with another bigger dataset with images (i.e ImageNet) and train a CNN on these merged examples. Take an already pre-trained deep neural network on a larger dataset, cut into it, and attach an additional “classification head” i.e. several additional fully connected layers with the Softmax layer on top of them. CNN popular in image Good at Preserving depth 3 channels for RGB ( get preserved) Several important Layer types – conv layer filters through inputs and see which inputs are important Pooling layers – reduce number of inputs to condense the information so that we don’t use too much memory/ too many parameters expensive and time consuming Fully connected layer – tell you the classification and probabilities
- Introduce the service, the value add of notebooks in this scenario, and then cover compute
- Azure Machine Learning Services empowers you to bring AI to everyone with an end-to-end, scalable, trusted platform. Boost your data science productivity Python pip-installable extensions for Azure Machine Learning that enable data scientists to build and deploy machine learning and deep learning models Now available for Computer Vision, Text Analytics and Time-Series Forecasting. Increase your rate of experimentation Rapidly prototype on your desktop, then easily scale up on virtual machines or scale out using Spark clusters Proactively manage model performance, identify the best model, and promote it using data-driven insights Collaborate and share solutions using popular Git repositories. Deploy and manage your models everywhere Use Docker containers to deploy models into production faster in the cloud, on-premises, or at the edge Promote your best performing models into production and retrain them when their performance degrades Azure Machine Learning Services are built with your needs in mind, providing: GPU-enabled virtual machines Low-latency predictions at scale Integration with popular Python IDEs Role-based access controls Model versioning Automated model retraining (Optional: other services) Azure Machine Learning Workbench integrates with ONNX models Work with your ONNX models from Visual Studio Code Tools for AI. Build deep learning models and call services straight from your favorite IDE easier with Azure Machine Learning services built right in. Create a seamless developer experience across desktop, cloud, or at the edge. AI Toolkit for Azure IoT Edge MMLSpark is an open-source Spark package that enables you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets by using deep learning and data science tools for Apache Spark. Azure Machine Learning Services seamlessly integrates with the rest of the Azure portfolio. <Transition>: Azure Machine Learning Services allows you to deploy models to many different production environments.
- Introduce the service, the value add of notebooks in this scenario, and then cover compute