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How can Azure superpower your data science journey?

Tania Allard
November 30, 2019

How can Azure superpower your data science journey?

This deck serves as an introduction to Azure Machine learning services.
it walks you through a brief intro of ML, Azure Machine learning services, Distributed hyperparameter tuning and Auto ML

Tania Allard

November 30, 2019
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  1. How can Azure superpower
    your Data Science journey?
    UCL data science hackathon
    2019-11-30
    Tania Allard, PhD
    ixek

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  2. About me
    Developer advocate @ Microsoft – scientific
    computing and machine learning
    Champion for open source in research and
    education
    Champion for diversity, inclusion and
    accesible tech
    PSF fellow
    GDE for Tensorflow

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  3. Outline
    3
    Azure Machine learning
    A 101 on using machine learning to
    solve problems
    Super quick intro to ML
    Getting started with AML
    Advanced features for ML
    Speeding up
    1
    2
    3

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  4. What is machine
    learning?
    Quick 101

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  5. What is AI, ML, and DL?
    Artificial intelligence (AI) is a technique that enables computers to
    mimic human intelligence. It includes machine learning.
    Machine learning (ML) is a subset of artificial intelligence that
    includes techniques (such as deep learning) that enable machines
    to improve at tasks with experience.
    Deep learning (DL) is a subset of machine learning based
    on artificial neural networks that permit a machine to train
    itself.

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  6. Traditional programming Machine learning

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  7. Machine learning

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  8. Machine learning
    FINDING (AND
    EXPLOITING) PATTERNS IN
    DATA
    REPLACING “HUMAN
    WRITING CODE” WITH
    “HUMAN SUPPLYING DATA”
    STARTS WITH A SHARP
    QUESTION

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  9. When is ML the
    right tool?
    Start by asking a question: What are
    the forecasted sales quantities per
    item in the next 4 weeks?

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  10. When should you use machine learning?
    ● Regression: how much / how many
    ● Classification: which class does it belong to?
    ● Clustering: are there different groups? Which does it belong
    to?
    ● Anomaly Detection: is this weird?
    ● Recommendation: which option should I choose?
    supervised
    learning
    unsupervised
    learning

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  11. https://cda.ms/17L

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  12. How can Azure help
    you to develop ML
    solutions?

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  13. Azure Bot Service
    Azure Cognitive
    Services
    Azure Cognitive Search
    Azure Machine Learning
    Knowledge mining
    AI apps & agents Machine learning
    Azure AI

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  14. Machine Learning on Azure
    Azure Machine Learning
    Domain Specific Pretrained Models
    To reduce time to market
    Azure
    Databricks
    Machine
    Learning VMs
    Popular Frameworks
    To build machine learning and deep learning
    solutions
    TensorFlow
    PyTorch ONNX
    Azure Machine
    Learning
    Language
    Speech

    Search
    Vision
    Productive Services
    To empower data science and development teams
    Powerful Hardware
    To accelerate deep learning
    Scikit-Learn
    PyCharm Jupyter
    Familiar Data Science Tools
    To simplify model development Visual Studio Code Command line
    CPU GPU FPGA

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  15. Azure Machine Learning
    Azure Cloud
    Services
    Python
    SDK
    ü Prepare Data
    ü Build Models
    ü Train Models
    ü Manage Models
    ü Track
    Experiments
    ü Deploy Models
    That enables you to:
    Cross-Platform
    CLI

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  16. Machine Learning
    Typical E2E Process
    Prepare
    Data
    Register and
    Manage Model
    Train &
    Test Model
    Build
    Image
    Build model
    (your favorite
    IDE)
    Deploy
    Service
    Monitor
    Model
    Prepare Experiment Deploy
    Orchestrate

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  17. Azure Machine Learning
    Datasets – registered, known data sets
    Experiments – Training runs
    Pipelines – Training workflows
    Models – Registered, versioned models
    Endpoints:
    Real-time Endpoints – Deployed model endpoints
    Pipeline Endpoints – Training workflow endpoints
    Compute – Managed compute
    Environments – defined training and inference
    environments
    Datastores – Connections to data

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  18. 19
    Familiar example: digits recognition

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  19. 20
    Familiar example: digits recognition

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  20. Workspace and
    compute

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  21. Azure Machine Learning
    Datasets – registered, known data sets
    Experiments – Training runs
    Pipelines – Training workflows
    Models – Registered, versioned models
    Endpoints:
    Real-time Endpoints – Deployed model endpoints
    Pipeline Endpoints – Training workflow endpoints
    Compute – Managed compute
    Environments – defined training and inference
    environments
    Datastores – Connections to data

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  22. Your favourite platforms
    23

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  23. Running experiments

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  24. Azure Machine Learning
    Datasets – registered, known data sets
    Experiments – Training runs
    Pipelines – Training workflows
    Models – Registered, versioned models
    Endpoints:
    Real-time Endpoints – Deployed model endpoints
    Pipeline Endpoints – Training workflow endpoints
    Compute – Managed compute
    Environments – defined training and inference
    environments
    Datastores – Connections to data

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  25. Deploying your models

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  26. Azure Machine Learning
    Datasets – registered, known data sets
    Experiments – Training runs
    Pipelines – Training workflows
    Models – Registered, versioned models
    Endpoints:
    Real-time Endpoints – Deployed model endpoints
    Pipeline Endpoints – Training workflow endpoints
    Compute – Managed compute
    Environments – defined training and inference
    environments
    Datastores – Connections to data

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  27. My Computer
    Data Store
    Azure ML
    Workspace
    Compute Target

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  28. The 8 Azure ML Train Steps

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  29. Deploy image
    Azure
    Kubernetes
    Service
    (AKS)
    Azure
    Container
    Instance

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  30. Some advanced
    AML features

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  31. Distributed
    Hyperparameter
    Tuning

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  32. 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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  33. 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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  34. Automated ML

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  35. Mileage
    Condition
    Car brand
    Year of make
    Regulations

    Parameter 1
    Parameter 2
    Parameter 3
    Parameter 4

    Gradient Boosted
    Nearest Neighbors
    SVM
    Bayesian Regression
    LGBM

    Mileage Gradient Boosted Criterion
    Loss
    Min Samples
    Split
    Min Samples Leaf
    Others
    Model
    Which algorithm? Which parameters?
    Which features?
    Car brand
    Year of make
    Model creation is typically a time-consuming process

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  36. Which algorithm? Which parameters?
    Which features?
    Mileage
    Condition
    Car brand
    Year of make
    Regulations

    Gradient Boosted
    Nearest Neighbors
    SGD
    Bayesian Regression
    LGBM

    Nearest Neighbors
    Criterion
    Loss
    Min Samples
    Split
    Min Samples Leaf
    XYZ
    Model
    Iterate
    Gradient Boosted N Neighbors
    Weights
    Metric
    P
    ZYX
    Mileage
    Car brand
    Year of make
    Car brand
    Year of make
    Condition
    Track
    Model creation is typically a time-consuming process

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  37. Track
    Which algorithm? Which parameters?
    Which features?
    Iterate
    Model creation is typically a time-consuming process

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  38. Enter data
    Define goals
    Apply
    constraints
    Output
    Automated Machine Learning accelerates
    model development
    Input Intelligently test multiple
    models in parallel
    Optimized model

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  39. Auto ML should not be an
    excuse for black-box ML

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  40. Experiment!
    This is only a taster of what you can do.
    Experiment with multiple scenarios,
    techniques and products
    Get your
    Azure
    subscription
    and claim
    your $100

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  41. Useful resources
    • GitHub Repository : https://github.com/trallard/ML-in-AML
    • Azure Machine learning
    • Create development environment for Machine learning
    • Hyperparameter tuning in AML
    • AML Python SDK
    • AML Pipelines

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  42. Useful resources
    • Getting started with Auto ML
    • Intro to AML – MS Learn
    • Automate model select with AML - MS Learn
    • Train local model with AML - MS Learn

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  43. Tania Allard, PhD
    @ixek
    56
    Thanks!

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