AI with Azure Machine Learning
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Slide deck used to introduce machine learning with Azure Machine Learning Service. Focus on deployment of models with the machine learning SDK and consumption of the models with Python and Go.
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AI with Azure Machine Learning
- 1. Focus on model deployment AI with Azure Machine Learning
- 2. Pre-Built AI Azure Cognitive Services Conversational AI Azure Bot Service Custom AI Azure Machine Learning
- 5. 6
- 6. “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” -- Arthur Samuel, 1959
- 7. Types of machine learning 8 Supervised Unsupervised Semi supervised Reinforcement learning
- 8. Supervised learning ▪ Training data includes the desired solution (called labels) and features ▪ E.g. predict house prices based on features of the house and neighborhood; you have a data set that contains the house prices ▪ Classification (e.g. spam filter), regression (e.g. house price prediction) 9
- 9. Unsupervised learning ▪ Training data does not contain labels 10
- 10. 2016: 96% accuracy RESNET vision test (152 layer neural network) 2017: 5.1% switchboard speech recognition test Jan 2018: 88.5% SQuAD reading comprehension test March 2018: 69.9% MT research system 11
- 11. Challenges ▪ Not enough data: thousands or millions of examples required depending on the problem ▪ Data is not representative ▪ Sampling noise ▪ Sampling bias ▪ Poor quality of data: importance of “data cleansing” ▪ Errors ▪ Outliers ▪ Noise ▪ Irrelevant features ▪ Overfitting and underfitting 12
- 12. How does it work? The “Hello World” of machine learning
- 13. Linear Regression? Just use Excel! y=b + w * x
- 14. But how can we find the parameters b and w? ▪ Linear regression has a closed-form solution: the normal equation ▪ Calculates b and w directly ▪ Slow if there are many features ▪ Learn the parameters iteratively ▪ “Gradient Descent” algorithm ▪ Just select a random b and w and check the cost ▪ Modify b and w to obtain a lower cost and keep doing that for a number of times 15
- 15. Cost function ▪ Basically measures the difference between the real values (blue) and predicted values (red) ▪ There is a red line with the lowest cost ▪ The goal is to find that line by “cleverly” trying new combinations of b and w 16
- 17. Learning rate: size of the steps you take 18
- 18. Not just for straight lines 19 y=w1 * x^2 + w2 * x + b
- 19. Demo ▪ Linear Regression in a Jupyter Notebook 20
- 20. 21
- 21. Training models Just the important bits to understand how models are built
- 22. Wait a moment! What’s Machine Learning? ▪ We will talk about supervised learning only ▪ Learn from labeled data e.g. you have images of numbers and you know the actual number the image represents e.g. you have images of cats and you know they are cats ▪ Make predictions on new samples Learn a model predict “label = 4” (based on the model)
- 23. Storage Training a model (running experiments) ComputeTrain.py
- 24. Python SDK ▪ Connect to your workspace ▪ Provision compute targets ▪ Upload data to storage ▪ Submit experiments ▪ Log training process ▪ Register models ▪ Deprovision compute targets ▪ Create container images ▪ Deploy as a web service ▪ ...
- 27. Training a model with train.py ▪ Load data from storage ▪ Fit ML model on the data ▪ use your framework of choice ▪ Save the model to storage ▪ Log metrics (e.g. accuracy) to Azure ML
- 29. Guided Exercise ▪ Log on to Azure Portal ▪ Create Azure ML service ▪ Log on to Azure Notebooks ▪ Perform the Getting Started exercise (Monte Carlo simulation to calculate Pi) ▪ uses local compute (of the workbook) 30
- 30. Packaging Models aka operationalizing models aks run models in production
- 32. Scoring with score.py ▪ To make predictions (inference) you provide a score.py script ▪ Azure ML uses score.py to build an API you can deploy to computer targets ▪ The API is packaged in a Docker container def init(): load(model) def run(input) prediction = model(input) return prediction
- 33. Storage Custom models: Machine Learning Service ComputeTrain.py Container image Score.py Registered model Deploy ?
- 34. ONNX Models We will use ONNX models and package them in a container
- 37. Why use ONNX? ▪ Better interoperability ▪ Spend less time to put models into production ▪ Ecosystem of tools for visualization and acceleration ▪ Converters ▪ Visualizers
- 39. Guided Exercise ▪ Create a container image that uses the ResNet50 model to classify images 40
- 40. Deploying Models
- 41. Deploying models ▪ A model is a function to execute ▪ A model is deployed as a REST API endpoint that you call from your custom application ▪ Inference = making decisions about new data e.g. is it a cat?
- 42. Azure Container Instances Azure Kubernetes Service Azure IoT Edge Field- programmable gate array (FPGA)
- 43. Deploy to Azure Container Instances (ACI) ▪ Simplest way to run containers in Azure ▪ No need to think about clusters ▪ Container groups with multiple containers
- 44. Deploy to Azure Kubernetes Service (AKS) ▪ For high-scale production scenarios ▪ Use an existing AKS cluster or deploy from Azure ML ▪ More advanced capabilities ▪ Autoscaling ▪ Use of GPUs ▪ Monitoring and logging
- 46. GPUs on Kubernetes ▪ Deploy on GPU instances ▪ Ensure GPU capabilities are advertised ▪ use device plugins ▪ You need to specifically enable GPU support in the container used for inference
- 47. Guided Exercise ▪ Deploying the ResNet50 model to Azure Container Instances ▪ Inference with Python ▪ Inference with Go 50
- 48. On your own ▪ Deploying the FER+ (emotion recognition for faces) model to Azure Container Instances ▪ Inference with Python 51
- 50. Transfer Learning Image from Medium
- 54. Predicting with the Prediction URL ▪ Post a URL or binary data to the prediction URL ▪ For binary data:
- 55. Export the model ▪ Set the domain to general compact: model will be smaller and potentially less accurate ▪ Export is now possible
- 56. Dockerfile ▪ Build the image with the included Dockerfile: ▪ Push the image to a registry (e.g. Docker Hub: ▪ docker push gbaeke/carmake
- 57. Run in ACI
- 59. Running models at the edge IoT Edge to the rescue
- 60. Deploy to IoT Edge ▪ IoT Edge provides the capability to process telemetry at the network edge ▪ Process data locally and forward to the cloud when needed ▪ e.g. alert, run AI model, … ▪ IoT Edge uses modules and these modules are containers ▪ Use IoT Hub to deploy an Azure ML container to the edge device
- 61. Example of deployment to IoT Edge
- 62. FPGA
- 63. Deploy to FPGAs ▪ Array of programmable logic blocks and reconfigurable interconnects ▪ Configurable post-manufacturing ▪ Mainly used for inference (not for training)
- 64. Project Brainwave on Azure ▪ Based on Intel FPGA devices ▪ Available in Azure for real-time AI (inference) ▪ Configurable for different types of pre- trained deep neural networks (DNN) ▪ Image classification and recognition ▪ ResNet 50, ResNet 152, VGG-16, … ▪ High performance > 500 classifications / second ▪ Not for your custom models