Getting started with Azure Machine Learning service

With machine learning becoming more and more an engineering problem the need to track, work together and easily deploy ML experiments with integrated CI/CD tooling is becoming more relevant then ever.

In this session we take a deep-dive into Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy, and manage models. We zoom into the building blocks provided and show, through some demos, how to use them.

At the end of this session you have a good grasp of the technological building blocks of Azure machine learning services. Just waiting to be used in your own projects afterwards.

Transcript

1. Henk Boelman Cloud Advocate @ Microsoft Getting started with Azure Machine Learning studio HenkBoelman.com @hboelman
2. Agenda Machine Learning on Azure Data science process Simpson recognition using Azure Machine Learning studio
3. Machine Learning Ability to learn without being explicitly programmed.
4. Programming Algorithm Data Answers
5. Machine Learning Algorithm Data Answers
6. Machine Learning Model Data Answers
7. Machine Learning Model Data Answers
8. Machine Learning Predictions Data Model Data Answers
9. 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
10. Vision Speech Language Knowledge Cognitive Services: Pre-Trained models in the cloud and on the edge
11. …
12. Ask a sharp question Collect the data Prepare the data Select the algorithm Train the model Use the answer The data science process
13. Ask a sharp question
14. Ask a sharp question How much / how many? Is it this or that? Is it weird? Which group? Which action?
15. What is the price of a second hand car?
16. Collect the data
17. Collect the data
18. Collect the data
19. Prepare the data
20. Prepare the data Every column in your dataset has to be: Relevant Independent Simple Clean
21. Prepare the data: Relevant
22. Prepare the data: Independent
23. Prepare the data: Simple
24. Prepare the data: Clean
25. Prepare the data: Clean
26. Select the algorithm
27. Select the algorithm
28. Train the Model
31. Use the answer
33. Azure Machine Learning studio A fully-managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions.
34. Is it Marge or Homer?
36. 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
37. Prepare your environment Experiment with your model & data Deploy Your model into production
38. Step 1: Prepare your environment
39. Setup your environment VS Code Azure Notebooks Azure Portal
40. Azure Notebook / Jupyter Notebook
41. Create a workspace ws = Workspace.create( name='<NAME>', subscription_id='<SUBSCRIPTION ID>', resource_group='<RESOURCE GROUP>', location='westeurope') ws.write_config() ws = Workspace.from_config() Create a workspace
43. 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
44. Create Compute cfg = AmlCompute.provisioning_configuration( vm_size='STANDARD_NC6', min_nodes=1, max_nodes=6) cc = ComputeTarget.create(ws, '<NAME>', cfg) Create a workspace Create compute
46. Demo: Setup your workspace Create a workspace Create compute Setup storage
47. Step 1 Prepare your environment Create a workspace Create compute Setup storage
48. Step 2: Experiment with your model & data
49. Create an experiment exp = Experiment(workspace=ws, name=“<ExperimentName>”) Create an Experiment
50. Create a training file Create an Experiment Create a training file
51. 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
52. 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
53. Create an Experiment Create a training file Submit to the AI cluster Create an estimator Demo: Creating and run an experiment
54. 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
55. Register the model model = run.register_model( model_name='SimpsonsAI', model_path='outputs') Create an Experiment Create a training file Submit to the AI cluster Create an estimator Register the model
56. Create an Experiment Create a training file Submit to the AI cluster Create an estimator Register the model Demo: Register and test the model
57. Step 2 Experiment with your model & data
58. Step 3: Deploy your model into production
59. AMLS to deploy The Model Score.py Environment file Docker Image
60. 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)
61. 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')
62. Inference config inference_config = InferenceConfig( runtime= "python", entry_script="score.py", conda_file="myenv.yml" )
63. Deployment using AMLS
64. Deploy to ACI aciconfig = AciWebservice.deploy_configuration( cpu_cores = 1, memory_gb = 2) service = Model.deploy(workspace=ws, name='simpsons-aci', models=[model], inference_config=inference_config, deployment_config=aciconfig)
65. Deploy to AKS aks_target = AksCompute(ws,"AI-AKS-DEMO") deployment_config = AksWebservice.deploy_configuration( cpu_cores = 1, memory_gb = 1) service = Model.deploy(workspace=ws, name="simpsons-ailive", models=[model], inference_config=inference_config, deployment_config=deployment_config, deployment_target=aks_target) service.wait_for_deployment(show_output = True)
66. Demo: Deploy to ACI
67. Prepare Data Register and Manage Model Train & Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy
68. @hboelman github.com/hnky henkboelman.com Thank you! Read more on: henkboelman.com