Azure AutoML

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

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Sivamuthu Kumar

April 27, 2019
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Transcript

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    Agenda ML – Overview Azure Machine Learning What is Automated

    ML? Build a ML Model using Automated ML Demo
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    Hello !! I’m Siva Working as Architect in CEI #Cloud

    #Mobile #IoT Solutions ksivamuthu ksivamuthu
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    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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    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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    ML Workflow Gathering Data Preparing Data Choosing a model Training

    Evaluation Hyper parameter tuning Prediction
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    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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    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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    • 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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    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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    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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    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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    MODEL CREATION TIME – FROM DAYS TO HOURS ROBUST BENCHMARKING

    PROCESS FOR ML PROJECTS ENABLE DOMAIN EXPERTS TO LEVERAGE ML
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    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