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Training of Python scikit-learn models on Azure
Mark Tabladillo, Ph.D., is a cloud solution architect at Microsoft with a background in analytics and a doctorate from Georgia Tech. The document outlines the business value of AI and Microsoft's offerings in data modernization, machine learning, and cloud services, emphasizing tools and frameworks for data science and deep learning. It highlights Microsoft's commitment to security, compliance, and ethical AI across various platforms.
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Training of Python scikit-learn models on Azure
- 1. Mark Tabladillo Ph.D. MicrosoftConfidential
- 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 businessvalue 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* Mycompany 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 SalesInsights 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 modernizationto Azure Globally distributed data Cloud Scale Analytics Data Modernization on-premises AI apps & agents Knowledge mining
- 10. Machine Learning onAzure 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 Sciencetools 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 toeveryone 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 CPUsGPUs 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 IntelligentCloud to the Intelligent Edge Train and deploy Train and deploy Deploy Track models in production Capture model telemetry Retrain models
- 15. Deploy and managemodels on intelligent cloud and edge Train & deploy Train & deploy Deploy Track models in production Capture model telemetry Retrain models automatically
- 17. Customer use caseData 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
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