Henk Boelman Cloud Advocate @ Microsoft Train your own model using Azure Machine Learning HenkBoelman.com @hboelman 

Machine Learning Ability to learn without being explicitly programmed. 

Programming Algorithm Data Answers 

Machine Learning Algorithm Data Answers 

Machine Learning Model Data Answers 

Machine Learning Model Data Answers 

Machine Learning Predictions Data Model Data Answers 

Ask a sharp question Collect the data Prepare the data Select the algorithm Train the model Use the answer The data science process 

Azure Machine Learning A fully-managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions. 

What number is it? 

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Sophisticated pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlow Keras Pytorch Onnx Azure Machine Learning Language Speech … Azure Search Vision On-premises Cloud Edge Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Flexible deployment To deploy and manage models on intelligent cloud and edge Machine Learning on Azure Cognitive Services 

Prepare your environment Experiment with your model & data Deploy Your model into production 

Step 1: Prepare your environment 

Setup your environment VS Code Azure Notebooks Azure Portal 

Azure Notebook / Jupyter Notebook 

Create a workspace ws = Workspace.create( name='', subscription_id='', resource_group='', location='westeurope') ws.write_config() ws = Workspace.from_config() Create a workspace 

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Datasets – registered, known data sets Experiments – Training runs Models – Registered, versioned models Endpoints: Real-time Endpoints – Deployed model endpoints Pipeline Endpoints – Training workflows Compute – Managed compute Datastores – Connections to data Azure Machine Learning Service 

Create Compute cfg = AmlCompute.provisioning_configuration( vm_size='STANDARD_NC6', min_nodes=1, max_nodes=6) cc = ComputeTarget.create(ws, '', cfg) Create a workspace Create compute 

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Demo: Setup your workspace Create a workspace Create compute Setup storage 

Step 1 Prepare your environment Create a workspace Create compute Setup storage 

Step 2: Experiment with your model & data 

Create an experiment exp = Experiment(workspace=ws, name=“”) Create an Experiment 

Create a training file Create an Experiment Create a training file 

Create an estimator params = {'--data-folder': ws.get_default_datastore().as_mount()} estimator = TensorFlow( source_directory = script_folder, script_params = params, compute_target = computeCluster, entry_script = 'train.py’, use_gpu = True, conda_packages = ['scikit-learn','keras','opencv’], framework_version='1.10') Create an Experiment Create a training file Create an estimator 

Submit the experiment to the cluster run = exp.submit(estimator) RunDetails(run).show() Create an Experiment Create a training file Submit to the AI cluster Create an estimator 

Create an Experiment Create a training file Submit to the AI cluster Create an estimator Demo: Creating and run an experiment 

Azure Notebook Compute Target Experiment Docker Image Data store 1. Snapshot folder and send to experiment 2. create docker image 3. Deploy docker and snapshot to compute 4. Mount datastore to compute 6. Stream stdout, logs, metrics 5. Launch the script 7. Copy over outputs 

Register the model model = Model.register( ws, model_name=‘My Model Name', model_path='./savedmodel', description='My cool Model') Create an Experiment Create a training file Submit to the AI cluster Create an estimator Register the model 

Create an Experiment Create a training file Submit to the AI cluster Create an estimator Register the model Demo: Register and test the model 

Step 3: Deploy your model into production 

AMLS to deploy The Model Score.py Environment file Docker Image 

Score.py %%writefile score.py from azureml.core.model import Model def init(): model_root = Model.get_model_path('MyModel’) loaded_model = model_from_json(loaded_model_json) loaded_model.load_weights(model_file_h5) def run(raw_data): url = json.loads(raw_data)['url’] image_data = cv2.resize(image_data,(96,96)) predicted_labels = loaded_model.predict(data1) return json.dumps(predicted_labels) 

Environment File from azureml.core.runconfig import CondaDependencies cd = CondaDependencies.create() cd.add_conda_package('keras==2.2.2') cd.add_conda_package('opencv') cd.add_tensorflow_conda_package() cd.save_to_file(base_directory='./', conda_file_path='myenv.yml') 

Create the image image_config = ContainerImage.image_configuration( runtime= "python", execution_script="score.py", conda_file="myenv.yml") image = Image.create(name = “my-image", models = [model], image_config = image_config, workspace = ws) 

Deployment using AMLS 

Deploy to ACI aciconfig = AciWebservice.deploy_configuration( cpu_cores = 1, memory_gb = 2) aci_service = Webservice.deploy_from_image( deployment_config = aciconfig, image = image, name = ‘', workspace = ws) 

Demo: Deploy to ACI 

Prepare Data Register and Manage Model Train & Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy 

@hboelman github.com/hnky henkboelman.com Thank you! Read more on: henkboelman.com Aka.ms/AI4DEV02-amls-basics