Uploaded byİbrahim KIVANÇ
Microsoft & Machine Learning / Artificial Intelligence
The document outlines Microsoft's AI and machine learning portfolio, detailing various applications and services such as Azure Machine Learning, Cortana, and Cognitive Services. It highlights tools for data science, model management, and experimentation, emphasizing the flexibility of using different frameworks and libraries. Additionally, it discusses the integration of edge computing and the use of deep learning for advanced image recognition tasks.
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Microsoft & Machine Learning / Artificial Intelligence
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- 2. Machine Learning /Deep Learning
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- 4. Agent Applications ServicesInfrastructure Microsoft AI Portfolio Cortana Office 365 Dynamics 365 SwiftKey Pix Customer Service and Support Skype Calendar.help Cortana Intelligence Cognitive Services Bot Framework Cortana Devices SDK Cognitive Toolkit Azure Machine Learning Azure Notebooks Azure N Series FPGA People
- 6. Agent Applications ServicesInfrastructure Microsoft AI Portfolio Cortana Office 365 Dynamics 365 SwiftKey Pix Customer Service and Support Skype Calendar.help Cortana Intelligence Cognitive Services Bot Framework Cortana Devices SDK Cognitive Toolkit Azure Machine Learning Azure Notebooks Azure N Series FPGA People
- 7. H Y PE R S C A L E , E N T E R P R I S E - G R A D E I N F R A S T R U C T U R E D E V E L O P E R T O O L S & S E R V I C E S O P E N P L A T F O R M F O R D A T A S C I E N C E Hardware Storage management Software T H E M L & A I P L A T F O R M AI Applications (1st & 3rd party) Cognitive Services Bot Framework Spark AI Batch Training DS VM SQL Server ACS BLOB Cosmos DB SQL DB/DW ADLS CPUs FPGA GPUs IoT Azure Machine Learning Model deployment & management Machine Learning toolkits Experimentation management, data prep, & collaboration CNTK Tensorflow ML Server Scikit-Learn Other Libs. PROSE Docker Cloud – Spark, SQL, other engines ML Server – Spark, SQL, VMs Edge
- 9. Machine Learning &AI Portfolio When to use what? What engine(s) do you want to use? Deployment target Which experience do you want? Build your own or consume pre-trained models? Microsoft ML & AI products Build your own Azure Machine Learning Code first (On-prem) ML Server On-prem Hadoop SQL Server (cloud) AML (Preview) SQL Server Spark Hadoop Azure Batch DSVM Azure Container Service Visual tooling (cloud) AML Studio Consume Cognitive services, bots
- 10. VISUAL DRAG-AND-DROP CODE-FIRST AzureMachine Learning Studio & Workbench
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- 12. Use your favoriteIDE Leverage all types of data Use what you want U S E T H E M O S T P O P U L A R I N N O V A T I O N S U S E A N Y T O O L U S E A N Y F R A M E W O R K O R L I B R A R Y
- 15. AML Workbench Sample, understand,and prep data rapidly Support for Spark + Python + R (roadmap) Execute jobs locally, on remote VMs, Spark clusters, SQL on-premises Git-backed tracking of code, config, parameters, data, run history
- 16. • Column statistics: Numeric • Histogram • Value Counts • Box Plot • Scatter Plot • Time Series • Map Inspectors
- 18. Spark SQL Server Virtual machines GPUs Containerservices Notebooks Azure Machine Learning Workbench Visual Studio Code Tools for AI Visual Studio Tools for AI PyCharm SQL Server Machine Learning Server O N - P R E M I S E S E D G E C O M P U T I N G Azure IoT Edge Experimentation and Model Management A Z U R E M A C H I N E L E A R N I N G S E R V I C E S T R A I N & D E P LO Y O P T I O N S A Z U R E
- 19. Local machine Scale upto DSVM Scale out with Spark on HDInsight Azure Batch AI (Coming Soon) ML Server (Coming Soon) A ZURE ML EXPERIMENTATION Command line tools IDEs Notebooks in Workbench VS Code Tools for AI VS Tools for AI
- 20. DOCKER Single node deployment (cloud/on-prem) AzureContainer Service Azure IoT Edge Microsoft ML Server Spark clusters SQL Server (Coming Soon) A ZURE ML MODEL MANAGEMENT
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- 22. Data Science Virtual Machines (DSVM)DSVM – Windows Server 2016 DSVM – Linux – Ubuntu Deep Learning Virtual Machines
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- 25. Researchers tooka traditional machine learning approach • Example: HoG Detectors - Histogram of oriented gradients (HoG) features - Sliding window detector - SVM Classifier - Very fast OpenCV implementation (<100ms)
- 27. Deep Neural Networkfor Computer Vision cat? YES dog? NO car? NO Convolutional Layers Fully Connected Layers Complex Objects & Scenes (people, animals, cars, beach scene, etc.) Image Low-Level Features (lines, edges, color fields, etc.) High-Level Features (corners, contours, simple shapes) Object Parts (wheels, faces, windows, etc.)
- 31. Cognitive Toolkit Unlock deeperlearning A free, easy-to-use, open-source toolkit that trains deep learning algorithms to learn like the human brain. Microsoft Cognitive Toolkit
- 32. Clothing texture dataset: Canwe apply transfer learning to accurately classify clothing texture? LeopardStriped Dotted
- 33. Pre-Built CNN fromGeneral Task on Millions of Images Output Layer Stripped Outputs of penultimate layer of ImageNet Trained CNN provide excellent general purpose image features cat? YES dog? NO car? NO Classi fier e.g. SVM dotted?
- 34. Pre-Built CNN fromGeneral Task on Millions of Images Output Layer Stripped Using a pre-trained DNN, an accurate model can be achieved with thousands (or less) of labeled examples instead of millions cat? YES dog? NO car? NO dotted? Train one or more layers in new network
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