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Big Data Advanced Analytics on Microsoft Azure 201904

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Mark Tabladillo
Mark Tabladillo

This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).

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Big Data Advanced Analytics on Microsoft Azure • Mark Tabladillo Ph.D. • Cloud Solution Architect • Microsoft • April 27, 2019 This Photo by Unknown Author is licensed under CC BY-SA
Big Data Advanced Analytics on Microsoft Azure 201904
Big Data Advanced Analytics on Microsoft Azure 201904
Microsoft AIhttps://www.microsoft. com/en-us/ai
Outline Azure Technology Overview for Advanced Analytics Technology Demos Team Data Science Process: A Point of View on Development
Azure Technology Overview
Domain specific pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Familiar Data Science tools To simplify model development CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge Azure Notebooks JupyterVisual Studio Code Command line
Choose any python development environment And improve data science productivity PyCharmAzure NotebooksVisual Studio Code Command lineZeppelin Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension Jupyter Get started with AML on Azure Notebooks: http://aka.ms/aznotebooks-aml
+ To empower data science and development teams Develop models faster with automated machine learning Use any Python environment and ML frameworks Manage models across the cloud and the edge. Prepare data clean data at massive scale Enable collaboration between data scientists and data engineers Access machine learning optimized clusters Azure Machine Learning Python-based machine learning service Azure Databricks Apache Spark-based big-data service
Leverage your favorite deep learning frameworks AZURE ML SERVICE Increase your rate of experimentation Bring AI to the edge Deploy and manage your models everywhere TensorFlow MS Cognitive Toolkit PyTorch Scikit-Learn ONNX Caffe2 MXNet Chainer AZURE DATABRICKS Accelerate processing with the fastest Apache Spark engine Integrate natively with Azure services Access enterprise-grade Azure security
What to use when? + Customer journey Data Prep Build and Train Manage and Deploy Apache Spark / Big Data Python ML developer Azure ML service (Pandas, NumPy etc. on AML Compute) Azure ML service (OSS frameworks, Hyperdrive, Pipelines, Automated ML, Model Registry) Azure ML service (containerize, deploy, inference and monitor) Azure ML service (containerize, deploy, inference and monitor) Azure Databricks (Apache Spark Dataframes, Datasets, Delta, Pandas, NumPy etc.) Azure Databricks + Azure ML service (Spark MLib and OSS frameworks + Automated ML, Model Registry)
Register and Manage Model Build Image Build model (your favorite IDE) Deploy Service Monitor Model Train & Test Model Integrated with Azure DevOps
Technology Demos
Big Data Advanced Analytics on Microsoft Azure 201904
Team Data Science Process
Data Science Lifecycle •Primary phases: Lifecycle
Data Science Lifecycle •Primary phases: Lifecycle
Resources • Microsoft AI https://www.microsoft.com/en-us/ai • Team Data Science Process https://docs.microsoft.com/en- us/azure/machine-learning/team-data-science-process/ • Azure DevOps https://docs.microsoft.com/en- us/azure/devops/?view=azure-devops • AI Business School https://www.microsoft.com/en-us/ai/ai-business-school
Summary Azure Technology Overview for Advanced Analytics Technology Demos Team Data Science Process: A Point of View on Development
Connect with Mark Tabladillo Linked In Twitter @marktabnet

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Big Data Advanced Analytics on Microsoft Azure 201904

  • 1. Big Data Advanced Analytics on Microsoft Azure • Mark Tabladillo Ph.D. • Cloud Solution Architect • Microsoft • April 27, 2019 This Photo by Unknown Author is licensed under CC BY-SA
  • 5. Outline Azure Technology Overview for Advanced Analytics Technology Demos Team Data Science Process: A Point of View on Development
  • 7. Domain specific pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlowPyTorch ONNX Azure Machine Learning LanguageSpeech … SearchVision Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Scikit-Learn Familiar Data Science tools To simplify model development CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge Azure Notebooks JupyterVisual Studio Code Command line
  • 8. Choose any python development environment And improve data science productivity PyCharmAzure NotebooksVisual Studio Code Command lineZeppelin Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension Jupyter Get started with AML on Azure Notebooks: http://aka.ms/aznotebooks-aml
  • 9. + To empower data science and development teams Develop models faster with automated machine learning Use any Python environment and ML frameworks Manage models across the cloud and the edge. Prepare data clean data at massive scale Enable collaboration between data scientists and data engineers Access machine learning optimized clusters Azure Machine Learning Python-based machine learning service Azure Databricks Apache Spark-based big-data service
  • 10. Leverage your favorite deep learning frameworks AZURE ML SERVICE Increase your rate of experimentation Bring AI to the edge Deploy and manage your models everywhere TensorFlow MS Cognitive Toolkit PyTorch Scikit-Learn ONNX Caffe2 MXNet Chainer AZURE DATABRICKS Accelerate processing with the fastest Apache Spark engine Integrate natively with Azure services Access enterprise-grade Azure security
  • 11. What to use when? + Customer journey Data Prep Build and Train Manage and Deploy Apache Spark / Big Data Python ML developer Azure ML service (Pandas, NumPy etc. on AML Compute) Azure ML service (OSS frameworks, Hyperdrive, Pipelines, Automated ML, Model Registry) Azure ML service (containerize, deploy, inference and monitor) Azure ML service (containerize, deploy, inference and monitor) Azure Databricks (Apache Spark Dataframes, Datasets, Delta, Pandas, NumPy etc.) Azure Databricks + Azure ML service (Spark MLib and OSS frameworks + Automated ML, Model Registry)
  • 12. Register and Manage Model Build Image Build model (your favorite IDE) Deploy Service Monitor Model Train & Test Model Integrated with Azure DevOps
  • 15. Team Data Science Process
  • 18. Resources • Microsoft AI https://www.microsoft.com/en-us/ai • Team Data Science Process https://docs.microsoft.com/en- us/azure/machine-learning/team-data-science-process/ • Azure DevOps https://docs.microsoft.com/en- us/azure/devops/?view=azure-devops • AI Business School https://www.microsoft.com/en-us/ai/ai-business-school
  • 19. Summary Azure Technology Overview for Advanced Analytics Technology Demos Team Data Science Process: A Point of View on Development