Azure AutoML

Achieving state-of-the-art performance in a given data set is hard. It usually implies carefully selecting the right data pre-processing tasks, picking the algorithm, model, and architecture and pairing it with the right set of parameters. This end-to-end process is usually called Machine Learning Pipeline. Isn't tough and time-consuming?

Automated ML helps create a high-quality model using intelligent automation and optimization. You launch the automated machine learning process by establishing goals and constraints. Then allow the algorithm selection and hyperparameter tuning to happen for you. The AutoML technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.

In this session, we'll see how to set up the environment, configure an Azure Machine Learning service workspace and explore the results

Transcript

1. Azure AutoML LEARNING THE
   LEARNING
   

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2. Agenda
   ML – Overview
   Azure Machine Learning
   What is Automated ML?
   Build a ML Model using Automated ML
   Demo
   

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3. Hello !!
   I’m Siva
   Working as Architect in CEI
   #Cloud #Mobile #IoT Solutions
   ksivamuthu
   ksivamuthu
   

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4. Machine Learning … Everywhere …
   ML Overview
   

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5. Artificial Intelligence
   Science of getting machines to do the things what they do in movies. (Mimic human behavior)
   Machine Learning
   Subset of AI - The science of getting computers to act without being explicitly programmed.
   Deep Learning
   Subset of ML - Learning based on deep neural network.
   

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6. Machine Learning
   Using known data, develop a
   model to predict unknown data
   

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7. Machine learns the same way human do.
   

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8. Let’s do an exercise on
   human learning to
   understand machine
   learning.
   @ksivamuthu
   

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9. Virzeth
   https://medium.com/@yakubova92/machine-learning-vs-human-learning-f3f204c8b27d
   @ksivamuthu
   

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10. Virzeth
    

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11. Virzeth or Not ?
    @ksivamuthu
    

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12. Types of Machine Learning
    Supervised Learning Reinforcement
    Learning
    Train an algorithm to perform
    classification and regression with
    labelled data set.
    Unsupervised
    Learning
    Train an algorithm to find clusters and
    associations in an unlabeled data set.
    Train an agent to take certain actions
    in an environment without a data set.
    

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13. Energy Auto Finance Entertainment
    Media Manufacturing /
    Agriculture
    Retail
    

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14. ML Workflow
    Gathering Data Preparing Data Choosing a model
    Training Evaluation Hyper parameter
    tuning
    Prediction
    

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15. Data
    preprocessing
    ML Model Design
    Tune model
    parameters
    Evaluate Deploy Update
    What preprocessing techniques
    should I use?
    What modeling techniques?
    Which architecture?
    Which set of hyperparameters?
    How do I know my model’s
    performance?
    How can I improve my dataset?
    Which metrics are most important?
    What infrastructure to use to serve
    my model at scale?
    How to meet latency goals?
    ML Workflow
    

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16. Azure Machine
    Learning
    Service
    

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17. 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
    

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19. Creating ML Workspace
    

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20. Azure ML Workspace Resources
    

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21. ML
    EXPERIMENTS DATASTORE COMPUTE
    MODELS IMAGES DEPLOYMENTS
    

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23. Automated ML
    

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25. • Based on Microsoft
    Research
    • Brain trained with several
    million experiments
    • Collaborative filtering and
    Bayesian optimization
    • Privacy preserving: No need
    to “see” the data
    Automated ML – How it works
    

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26. Build a ML Model
    using AutoML
    

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27. Type of machine
    learning problem
    you are solving …
    

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28. Categories of
    supervised
    learning
    supported
    CLASSIFICATION REGRESSION
    FORECASTING
    

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31. Data
    preparation &
    Compute Target
    • Source and format of training data
    • Data can be read into Numpy arrays or a
    Pandas data frame
    • Configure split options for selecting
    training and validation data
    • You can specify separate training and
    validation datasets.
    • Configure compute target
    

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32. Configure
    your
    experiment
    settings
    automl_classifier = AutoMLConfig(
    task='classification’,
    primary_metric='AUC_weighted’,
    max_time_sec=12000,
    iterations=50,
    X=X,
    y=y,
    n_cross_validations=2
    )
    

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33. Primary Metric
    Classification Regression Time Series Forecasting
    accuracy spearman_correlation spearman_correlation
    AUC_weighted normalized_root_mean_squared
    _error
    normalized_root_mean_squared
    _error
    average_precision_score_weighte
    d
    r2_score r2_score
    norm_macro_recall normalized_mean_absolute_err
    or
    normalized_mean_absolute_err
    or
    precision_score_weighted
    

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34. Run
    Experiment run = experiment.submit(automl_config)
    

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35. Exit Criteria
    No Criteria
    Number of iterations / Iteration Timeout
    Minutes
    Exit after a length of time / Experiment
    Timeout Minute.
    Exit after a score has been reached –
    Experiment Exit Score
    

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36. MODEL CREATION TIME –
    FROM DAYS TO HOURS
    ROBUST BENCHMARKING
    PROCESS FOR ML PROJECTS
    ENABLE DOMAIN EXPERTS
    TO LEVERAGE ML
    

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37. Automated ML in
    Power BI
    

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39. Reference
    • Azure - https://azure.microsoft.com/en-
    us/services/machine-learning-service/
    • Microsoft Machine Learning Blog -
    https://azure.microsoft.com/en-us/blog/tag/azure-
    machine-learning/
    • Azure ML documentation -
    https://docs.microsoft.com/en-us/azure/machine-
    learning/
    • Slides - https://speakerdeck.com/ksivamuthu/azure-
    automl-learning-the-learning
    

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40. Thank You !! Follow me on social medias
    ksivamuthu
    ksivamuthu
    

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