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Mark Tabladillo Ph.D.
Microsoft Confidential
Mark Tabladillo Ph.D.
• Science doctorate from Georgia Tech
• Analytics career based on SAS,
Microsoft, open source
• (Part time): Graduate Business Faculty
• Tech Presentations:
• Seattle, Portland, Chicago, Boston,
Mountain View, San Francisco, San
Antonio, Charlotte, Orlando
• London, Hong Kong, Montreal
• Social Media
LinkedIn
Twitter @marktabnet
• Cloud Solution Architect
• US CTO Customer Success
3.9$ TGlobal business value derived
from AI in 2022 will reach
“Forecast: The Business Value of Artificial Intelligence, Worldwide, 2017-2025”, Gartner, April 2018.
Decision
support
Virtual
agents
Decision
automation
Smart
products
3.9$ T
My company NOW*
My company IN THE FUTURE (24-36 months)*
My top competitors NOW*
Analytics Capabilities
BASIC ADVANCED
*Based on independent market research in the form of focus groups and in-depth-interviews
Customer Insights Sales Insights Virtual Assistants Cash Flow Forecasting HR Insights
Churn Analytics Dynamic Pricing Waiting line optimization Risk Management Quality Assurance Resource Planning
Lead Scoring Intelligent chatbots Financial Forecasting Employee Insights
Marketing Sales Service Finance Operations Workforce
Product RecommendationProduct Recommendation Predictive MaintenancePredictive Maintenance
Demand ForecastingDemand Forecasting
https://www.microsoft.com/en-us/ai
Data AI
Data AI
Data modernization to Azure
Globally distributed data
Cloud Scale Analytics
Data Modernization on-premises AI apps & agents
Knowledge mining
Machine Learning on Azure
Domain specific pretrained models
To reduce time to market
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
PyCharm Jupyter
Familiar Data Science tools
To simplify model development
Visual Studio Code Command line
CPU GPU FPGA
From the Intelligent Cloud to the Intelligent Edge
Familiar Data Science tools
Choose any python development environment
And improve data science productivity
PyCharm Jupyter Visual Studio Code Command lineZeppelin
Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension
Bring AI to everyone with an end-to-end, scalable, trusted platform
Built with your needs in mind
Support for open source frameworks
Managed compute
DevOps for machine learning
Simple deployment
Tool agnostic Python SDK
Automated machine learning
Seamlessly integrated with the Azure Portfolio
Boost your data science productivity
Increase your rate of experimentation
Deploy and manage your models everywhere
Accelerate deep learning
CPUs GPUs FPGAs
General purpose
machine learning
D, F, L, M, H Series
Deep learning
N Series
Specialized hardware
accelerated deep learning
Project Brainwave
Optimized for flexibility Optimized for performance
From the Intelligent Cloud to the Intelligent Edge
Train and deploy Train and deploy
Deploy
Track models in production
Capture model telemetry
Retrain models
Deploy and manage models on intelligent cloud and edge
Train & deploy Train & deploy
Deploy
Track models in production
Capture model telemetry
Retrain models automatically
Customer use case Data Prep Build & Train Manage and Deploy
Big Data/Apache Spark
Azure Databricks
(Apache Spark Dataframes, Datasets, Delta,
Pandas, NumPy etc.)
Azure Databricks + Azure ML service
(Spark MLlib and OSS frameworks +
Automated ML, Model Registry)
Azure ML service
(containerize, deploy, inference and
monitor)
Data Science Pandas, NumPy etc.
Azure ML service
(OSS frameworks, Hyperdrive, Pipelines,
Automated ML, Model Registry)
Azure ML service
(containerize, deploy, inference and
monitor)
What to use when?
+
Azure Global Infrastructure
https://azure.microsoft.com/en-us/global-infrastructure/
Architecture
Cluster Parameters
Description
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/training-python-models
Cluster in Motion
Cluster Almost Done
DSVM Usage
Azure Storage Blob
MarkTab Modifications
Use any language, any development
tool and any framework
>90% of Fortune 500 companies
use Microsoft Cloud
Benefit from industry-leading security, privacy,
compliance, transparency, and AI ethics standards
Accelerate time to value
with agile tools and services
Powerful
tools
Pretrained AI
services
Comprehensive
platform
On-premisesEdgeCloud
Innovate with AI everywhere –
in the cloud, at edge and on-premises
https://academy.microsoft.com/en-us/professional-program/
https://www.microsoft.com/en-us/learning/default.aspx
https://channel9.msdn.com/
© Microsoft Corporation
Mark Tabladillo Ph.D.
Training of Python scikit-learn models on Azure

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Training of Python scikit-learn models on Azure

  • 2. Mark Tabladillo Ph.D. • Science doctorate from Georgia Tech • Analytics career based on SAS, Microsoft, open source • (Part time): Graduate Business Faculty • Tech Presentations: • Seattle, Portland, Chicago, Boston, Mountain View, San Francisco, San Antonio, Charlotte, Orlando • London, Hong Kong, Montreal • Social Media LinkedIn Twitter @marktabnet • Cloud Solution Architect • US CTO Customer Success
  • 3. 3.9$ TGlobal business value derived from AI in 2022 will reach “Forecast: The Business Value of Artificial Intelligence, Worldwide, 2017-2025”, Gartner, April 2018. Decision support Virtual agents Decision automation Smart products 3.9$ T
  • 4. My company NOW* My company IN THE FUTURE (24-36 months)* My top competitors NOW* Analytics Capabilities BASIC ADVANCED *Based on independent market research in the form of focus groups and in-depth-interviews
  • 5. Customer Insights Sales Insights Virtual Assistants Cash Flow Forecasting HR Insights Churn Analytics Dynamic Pricing Waiting line optimization Risk Management Quality Assurance Resource Planning Lead Scoring Intelligent chatbots Financial Forecasting Employee Insights Marketing Sales Service Finance Operations Workforce Product RecommendationProduct Recommendation Predictive MaintenancePredictive Maintenance Demand ForecastingDemand Forecasting https://www.microsoft.com/en-us/ai
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  • 8. Data AI Data modernization to Azure Globally distributed data Cloud Scale Analytics Data Modernization on-premises AI apps & agents Knowledge mining
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  • 10. Machine Learning on Azure Domain specific pretrained models To reduce time to market 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 PyCharm Jupyter Familiar Data Science tools To simplify model development Visual Studio Code Command line CPU GPU FPGA From the Intelligent Cloud to the Intelligent Edge
  • 11. Familiar Data Science tools Choose any python development environment And improve data science productivity PyCharm Jupyter Visual Studio Code Command lineZeppelin Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension
  • 12. Bring AI to everyone with an end-to-end, scalable, trusted platform Built with your needs in mind Support for open source frameworks Managed compute DevOps for machine learning Simple deployment Tool agnostic Python SDK Automated machine learning Seamlessly integrated with the Azure Portfolio Boost your data science productivity Increase your rate of experimentation Deploy and manage your models everywhere
  • 13. Accelerate deep learning CPUs GPUs FPGAs General purpose machine learning D, F, L, M, H Series Deep learning N Series Specialized hardware accelerated deep learning Project Brainwave Optimized for flexibility Optimized for performance
  • 14. From the Intelligent Cloud to the Intelligent Edge Train and deploy Train and deploy Deploy Track models in production Capture model telemetry Retrain models
  • 15. Deploy and manage models on intelligent cloud and edge Train & deploy Train & deploy Deploy Track models in production Capture model telemetry Retrain models automatically
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  • 17. Customer use case Data Prep Build & Train Manage and Deploy Big Data/Apache Spark Azure Databricks (Apache Spark Dataframes, Datasets, Delta, Pandas, NumPy etc.) Azure Databricks + Azure ML service (Spark MLlib and OSS frameworks + Automated ML, Model Registry) Azure ML service (containerize, deploy, inference and monitor) Data Science Pandas, NumPy etc. Azure ML service (OSS frameworks, Hyperdrive, Pipelines, Automated ML, Model Registry) Azure ML service (containerize, deploy, inference and monitor) What to use when? +
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  • 29. Use any language, any development tool and any framework >90% of Fortune 500 companies use Microsoft Cloud Benefit from industry-leading security, privacy, compliance, transparency, and AI ethics standards Accelerate time to value with agile tools and services Powerful tools Pretrained AI services Comprehensive platform On-premisesEdgeCloud Innovate with AI everywhere – in the cloud, at edge and on-premises