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).

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

4. Microsoft AIhttps://www.microsoft.
com/en-us/ai

5. Outline
Azure Technology Overview for
Advanced Analytics
Technology Demos
Team Data Science Process: A
Point of View on Development

6. Azure Technology Overview

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

13. Technology Demos

15. Team Data Science Process

16. Data Science Lifecycle
•Primary phases:
Lifecycle

17. Data Science Lifecycle
•Primary phases:
Lifecycle

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

20. Connect with
Mark
Tabladillo
Linked In
Twitter @marktabnet