Azure AutoML LEARNING THE LEARNING 

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

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

Machine Learning … Everywhere … ML Overview 

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. 

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

Machine learns the same way human do. 

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

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

Virzeth 

Virzeth or Not ? @ksivamuthu 

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. 

Energy Auto Finance Entertainment Media Manufacturing / Agriculture Retail 

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

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 

Azure Machine Learning Service 

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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Creating ML Workspace 

Azure ML Workspace Resources 

ML EXPERIMENTS DATASTORE COMPUTE MODELS IMAGES DEPLOYMENTS 

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Automated ML 

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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 

Build a ML Model using AutoML 

Type of machine learning problem you are solving … 

Categories of supervised learning supported CLASSIFICATION REGRESSION FORECASTING 

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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 

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 ) 

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 

Run Experiment run = experiment.submit(automl_config) 

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 

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

Automated ML in Power BI 

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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 

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