Taking Models to the Next Level With Azure Machine Learning

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aka.ms/AIML40 Henk Boelman (@hboelman)

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aka.ms/AIML40 Henk Boelman (@hboelman) In this session

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aka.ms/AIML40 Henk Boelman (@hboelman) The Story Existing pre-built models for text analytics Custom models trained on specific data

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aka.ms/AIML40 Henk Boelman (@hboelman)

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aka.ms/AIML40 Henk Boelman (@hboelman)

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aka.ms/AIML40 Henk Boelman (@hboelman) /Docs alert aka.ms/TextCogSvc Get started with Azure Cognitive Services Text Analytics API

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aka.ms/AIML40 Henk Boelman (@hboelman) From Cognitive Services to Azure Machine Learning

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aka.ms/AIML40 Henk Boelman (@hboelman)

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aka.ms/AIML40 Henk Boelman (@hboelman)

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aka.ms/AIML40 Henk Boelman (@hboelman) What is Azure Machine Learning? Set of Azure Cloud Services Azure CLI Extension ✓Prepare Data ✓Build Models ✓Train Models ✓Manage Models ✓Track Experiments ✓Deploy Models That enables you to: Python SDK Web interface

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aka.ms/AIML40 Henk Boelman (@hboelman) Advanced Azure ML Services

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aka.ms/AIML40 Henk Boelman (@hboelman) NLP-Architect Aspect Based Sentiment Analysis

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aka.ms/AIML40 Henk Boelman (@hboelman) Managing ML Challenges

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aka.ms/AIML40 Henk Boelman (@hboelman) Challenges of distributed training  Dependencies and Containers Provision clusters of VMs Schedule jobs Distribute data Gather results Handle failures Scale resources Secure Access

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aka.ms/AIML40 Henk Boelman (@hboelman) Azure Machine Learning Typical end to end ML process … Prepare Experiment Deploy Orchestrate

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aka.ms/AIML40 Henk Boelman (@hboelman) Prepare your environment Experiment with your model & data Deploy your model

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aka.ms/AIML40 Henk Boelman (@hboelman) Step 1: Prepare your environment

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aka.ms/AIML40 Henk Boelman (@hboelman) 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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aka.ms/AIML40 Henk Boelman (@hboelman)

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aka.ms/AIML40 Henk Boelman (@hboelman) Pipelines – Training workflows Pipeline Endpoints – Training workflow endpoints Azure Machine Learning

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aka.ms/AIML40 Henk Boelman (@hboelman) Create Compute cfg = AmlCompute.provisioning_configuration( vm_size='STANDARD_NC6', min_nodes=0, max_nodes=6) cc = ComputeTarget.create(ws, '', cfg) Create a workspace Create compute

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aka.ms/AIML40 Henk Boelman (@hboelman) Attach storage ds = ws.get_default_datastore() ds = Datastore.register_azure_blob_container( workspace = ws, datastore_name = ‘', container_name = ‘', account_name = ‘’, account_key = ‘', Create a workspace Create compute Setup storage

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aka.ms/AIML40 Henk Boelman (@hboelman) Step 1 Prepare your environment Create a workspace Create compute Setup storage

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aka.ms/AIML40 Henk Boelman (@hboelman) Step 2: Experiment with your model & data

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aka.ms/AIML40 Henk Boelman (@hboelman) Create an experiment exp = Experiment(workspace=ws, name=“”) Create an Experiment

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aka.ms/AIML40 Henk Boelman (@hboelman) Create a training file Create an Experiment Create a training file

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aka.ms/AIML40 Henk Boelman (@hboelman) Create an estimator params = {'--data-folder': ws.get_default_datastore().as_mount()} estimator = Estimator( source_directory = script_folder, script_params = params, compute_target = computeCluster, entry_script = 'train.py’, conda_packages = ['scikit-learn','keras','opencv’]) Create an Experiment Create a training file Create an estimator

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aka.ms/AIML40 Henk Boelman (@hboelman) 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

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aka.ms/AIML40 Henk Boelman (@hboelman) Create an Experiment Create a training file Submit to the AI cluster Create an estimator Demo: Creating and run an experiment

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aka.ms/AIML40 Henk Boelman (@hboelman) Notebook VM 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

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aka.ms/AIML40 Henk Boelman (@hboelman) Register the model model = run.register_model( model_name='MyAwesomeModel', model_path='outputs') Create an Experiment Create a training file Submit to the AI cluster Create an estimator Register the model

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aka.ms/AIML40 Henk Boelman (@hboelman) Create an Experiment Create a training file Submit to the AI cluster Create an estimator Register the model Demo: Register and test the model

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aka.ms/AIML40 Henk Boelman (@hboelman) Step 3: Deploy your model

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aka.ms/AIML40 Henk Boelman (@hboelman) AMLS to deploy The Model Score.py Environment file Docker Image

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aka.ms/AIML40 Henk Boelman (@hboelman) Score.py

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aka.ms/AIML40 Henk Boelman (@hboelman) Environment File

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aka.ms/AIML40 Henk Boelman (@hboelman) Inference config python score.py myenv.yml

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aka.ms/AIML40 Henk Boelman (@hboelman) Deployment using AML

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aka.ms/AIML40 Henk Boelman (@hboelman) Deploy to ACI ignitett-aci model inference_config aci_deployment_config

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aka.ms/AIML40 Henk Boelman (@hboelman) Deploy to AKS ignitett-aks model inference_config deployment_config aks_target

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aka.ms/AIML40 Henk Boelman (@hboelman) Demo: Deploy to ACI & AKS

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aka.ms/AIML40 Henk Boelman (@hboelman) Distributed Hyperparameter Tuning

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aka.ms/AIML40 Henk Boelman (@hboelman) What are Hyperparameters? { “learning_rate”: uniform(0, 1), “num_layers”: choice(2, 4, 8) … } Config1= {“learning_rate”: 0.2, “num_layers”: 2, …} Config2= {“learning_rate”: 0.5, “num_layers”: 4, …} Config3= {“learning_rate”: 0.9, “num_layers”: 8, …} …

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aka.ms/AIML40 Henk Boelman (@hboelman) Typical ‘manual’ approach to hyperparameter tuning Dataset Training Algorithm 1 Hyperparameter Values – config 1 Model 1 Hyperparameter Values – config 2 Model 2 Hyperparameter Values – config 3 Model 3 Model Training Infrastructure Training Algorithm 2 Hyperparameter Values – config 4 Model 4 Complex Tedious Repetitive Time consuming Expensive

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aka.ms/AIML40 Henk Boelman (@hboelman) Automated Hyperparameter Tuning Manage Active Jobs Evaluate training runs for specified primary metric Early terminate poor performing training runs. Early termination policies include: Early terminate poorly performing runs

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aka.ms/AIML40 Henk Boelman (@hboelman) Demo: hyperparameter with Azure Machine Learning

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aka.ms/AIML40 Henk Boelman (@hboelman) Resources aka.ms/AIML40repo aka.ms/AIML40 aka.ms/mymsignitethetour aka.ms/9TipsProdML

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