Download as PDF, PPTX
![Content of Image:
Categories v0: [{ “name”: “animal”, “score”: 0.9765625 }]
V1: [{ "name": "grass", "confidence": 0.9999992847442627 },
{ "name": "outdoor", "confidence": 0.9999072551727295 },
{ "name": "cow", "confidence": 0.99954754114151 },
{ "name": "field", "confidence": 0.9976195693016052 },
{ "name": "brown", "confidence": 0.988935649394989 },
{ "name": "animal", "confidence": 0.97904372215271 },
{ "name": "standing", "confidence": 0.9632768630981445 },
{ "name": "mammal", "confidence": 0.9366017580032349, "hint": "animal" },
{ "name": "wire", "confidence": 0.8946959376335144 },
{ "name": "green", "confidence": 0.8844101428985596 },
{ "name": "pasture", "confidence": 0.8332059383392334 },
{ "name": "bovine", "confidence": 0.5618471503257751, "hint": "animal" },
{ "name": "grassy", "confidence": 0.48627158999443054 },
{ "name": "lush", "confidence": 0.1874018907546997 },
{ "name": "staring", "confidence": 0.165890634059906 }]
Describe
0.975 "a brown cow standing on top of a lush green field“
0.974 “a cow standing on top of a lush green field”
0.965 “a large brown cow standing on top of a lush green field”
Computer Vision API](https://image.slidesharecdn.com/20190329aiworkshop-final-190611131648/85/Discover-AI-with-Microsoft-Azure-19-320.jpg)
![LUIS [Language Understanding Intelligent Service]
Create your own LU model
Train by providing examples
Deploy to an HTTP endpoint
and activate on any device
Maintain model with ease
https://www.luis.ai/home](https://image.slidesharecdn.com/20190329aiworkshop-final-190611131648/85/Discover-AI-with-Microsoft-Azure-37-320.jpg)
![{
“entities”: [
{
“entity”: “flight_delays”,
“type”: “Topic”
}
],
“intents”: [
{
“intent”: “FindNews”,
“score”: 0.99853384
},
{
“intent”: “None”,
“score”: 0.07289317
},
{
“intent”: “ReadNews”,
“score”: 0.0167122427
},
{
“intent”: “ShareNews”,
“score”: 1.0919299E-06
}
]
}
“News about
flight delays”
Language Understanding Models](https://image.slidesharecdn.com/20190329aiworkshop-final-190611131648/85/Discover-AI-with-Microsoft-Azure-38-320.jpg)
![Spellcheck examples
{
"offset": 48,
"token": "grate",
"type": "UnknownToken",
"suggestions": [
{
"suggestion": "great",
"score": "0.949079025281377"
}]
}
{
"offset": 23,
"token": "Seattle. hope",
"type": "UnknownToken",
"suggestions": [
{
"suggestion": "Seattle. Hope",
"score": "0.875"
}]
}
Hello Build! Welcome to Seattle. hope you have a grate week](https://image.slidesharecdn.com/20190329aiworkshop-final-190611131648/85/Discover-AI-with-Microsoft-Azure-44-320.jpg)
![Azure Notebooks – Toolbar and Shortcuts
Mode What Shortcut
Command (Press Esc to enter) Run cell Shift-Enter
Command Add cell below B
Command Add cell above A
Command Delete a cell d-d
Command Go into edit mode Enter
Edit (Press Enter to enable) Run cell Shift-Enter
Edit Indent Clrl-]
Edit Unindent Ctrl-[
Edit Comment section Ctrl-/
Edit Function introspection Shift-Tab](https://image.slidesharecdn.com/20190329aiworkshop-final-190611131648/85/Discover-AI-with-Microsoft-Azure-117-320.jpg)
Uploaded byJürgen Ambrosi
Discover AI with Microsoft Azure
This document provides an agenda for an AI workshop that covers various Microsoft AI technologies including computer vision, speech, and language. The agenda includes discussions on Microsoft's breakthroughs in computer vision by winning ImageNet competitions five years in a row. It also covers Microsoft's speech breakthroughs and ongoing momentum. The bulk of the agenda focuses on demonstrating various Microsoft Cognitive Services like Vision, Speech, Language, Translator, LUIS, and Bing APIs. It provides examples of calling the Computer Vision and Translator APIs and summarizes several Cognitive Services like Text Analytics, Spell Check, and Language Understanding. The document aims to educate attendees on Microsoft's broad portfolio of AI services and tools.
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Similar to Discover AI with Microsoft Azure
Discover AI with Microsoft Azure
- 2.
- 6. Vision Speech Language MicrosoftAI breakthroughs
- 7. ImageNet ILSVRC Worldwide IndustryCompetition for Object Recognition 28,2 25,8 16,4 11,7 7,3 6,7 5,1 3.5 ILSVRC 2010 NEC America ILSVRC 2011 Xerox ILSVRC 2012 AlexNet ILSVRC 2013 Clarifi ILSVRC 2014 VGG ILSVRC 2014 GoogleNet Human Performance ILSVRC 2015 ResNet Microsoft Classification Error Rate (%) Microsoft Researchers win in all 5 entries of ImageNet 2015 Classification, Localization, Detection, COCO detection COCO segmentation fish? stone?
- 8. Microsoft’s speech breakthrough Microsoftresearchers from the Speech & Dialogue research group 2017
- 10. Vision Speech Language OngoingMomentum
- 12.
- 13. • Easy • Rollyour own with REST APIs • Simple to add: just a few lines of code required • Flexible • Make the same API code call on iOS, Android, and Windows • Integrate into the language and platform of your choice • Tested • Built by experts in their field from Microsoft Research, Bing, and Azure Machine Learning • Quality documentation, sample code, and community support
- 14. Create a seamlessdeveloper experience across desktop, cloud, or at the edge using Visual Studio AI Tools Bot services Infuse intelligence into your bot using cognitive services Speed development with a purpose-built environment for bot creation Integrate across multiple channels to reach more customers Cognitive services Map complex information and data Use pre-built AI services to solve business problems Allow your apps to process natural language Azure search Reduce complexity with a fully-managed service Get up and running quickly Use artificial intelligence to extract insights
- 16. Cognitive Services Emotion Computer Vision Face Video SpeakerRecognition Speech Custom Recognition Translator Linguistic Analysis Language Understanding Bing Spell Check WebLM Text Analytics Entity Linking Knowledge Exploration Academic Knowledge Recommendations Bing Image Search Bing Video Search Bing Web Search Bing Autosuggest Bing News Search
- 17.
- 18.
- 19. Content of Image: Categoriesv0: [{ “name”: “animal”, “score”: 0.9765625 }] V1: [{ "name": "grass", "confidence": 0.9999992847442627 }, { "name": "outdoor", "confidence": 0.9999072551727295 }, { "name": "cow", "confidence": 0.99954754114151 }, { "name": "field", "confidence": 0.9976195693016052 }, { "name": "brown", "confidence": 0.988935649394989 }, { "name": "animal", "confidence": 0.97904372215271 }, { "name": "standing", "confidence": 0.9632768630981445 }, { "name": "mammal", "confidence": 0.9366017580032349, "hint": "animal" }, { "name": "wire", "confidence": 0.8946959376335144 }, { "name": "green", "confidence": 0.8844101428985596 }, { "name": "pasture", "confidence": 0.8332059383392334 }, { "name": "bovine", "confidence": 0.5618471503257751, "hint": "animal" }, { "name": "grassy", "confidence": 0.48627158999443054 }, { "name": "lush", "confidence": 0.1874018907546997 }, { "name": "staring", "confidence": 0.165890634059906 }] Describe 0.975 "a brown cow standing on top of a lush green field“ 0.974 “a cow standing on top of a lush green field” 0.965 “a large brown cow standing on top of a lush green field” Computer Vision API
- 20.
- 21.
- 22.
- 23.
- 24.
- 25.
- 26.
- 27. Custom Vision Services:Best Practices
- 28. Custom Vision Services:Example Scenarios
- 29.
- 30. Speech Translation Service Clientapp Translated text Translator
- 31.
- 32. Translate Speech -Close to real-time
- 33. Example of aspeech translation method call
- 34.
- 35.
- 36.
- 37. LUIS [Language UnderstandingIntelligent Service] Create your own LU model Train by providing examples Deploy to an HTTP endpoint and activate on any device Maintain model with ease https://www.luis.ai/home
- 38. { “entities”: [ { “entity”: “flight_delays”, “type”:“Topic” } ], “intents”: [ { “intent”: “FindNews”, “score”: 0.99853384 }, { “intent”: “None”, “score”: 0.07289317 }, { “intent”: “ReadNews”, “score”: 0.0167122427 }, { “intent”: “ShareNews”, “score”: 1.0919299E-06 } ] } “News about flight delays” Language Understanding Models
- 39.
- 40.
- 41. The Future OfOrder Taking at McDonalds
- 42.
- 43.
- 44. Spellcheck examples { "offset": 48, "token":"grate", "type": "UnknownToken", "suggestions": [ { "suggestion": "great", "score": "0.949079025281377" }] } { "offset": 23, "token": "Seattle. hope", "type": "UnknownToken", "suggestions": [ { "suggestion": "Seattle. Hope", "score": "0.875" }] } Hello Build! Welcome to Seattle. hope you have a grate week
- 45. Language understanding inhuman-computer interaction
- 46. Bing Search APIv5 REST Enhanced Search and Filtering Capabilities Ongoing Improvements and Support Web- Scale High Performance Secure (HTTPs)
- 47. *screenshots show actualsearch results on bing.com { “_type”: “SearchResponse”, “queryContent”: {…}, “webPages”: {…}, “news”: {…}, “images”: {…}, “videos”: {…}, “relatedSearches”: {…}, “rankingResponse”: {…} } { “answerType”:”WebPages”, “resultIndex”:0,… }, { “answerType”:”News”, “resultIndex”:1,… } Ranking Response Search Response Web Results Deep Links (1st Algo) News Results Image Results Video Results Related Searches Web Search API https://bingapis.azure-api.net/v5/search?q=nasa
- 48. Vertical Search APIs https://bingapis.azure-api.net/v5/images/search?q=shuttle+launch •Enhanced metadata and filters (size, license, style, freshness, color) • Image insights (entity recognition, visually similar) source: nasa.gov https://bingapis.azure-api.net/v5/videos/search?q=viral+videos • Enhanced metadata and filters (price, resolution, length, freshness) • Motion thumbnails (video preview) source: youtube.com https://bingapis.azure-api.net/v5/news/search?q=cuba • News by category/market, and trending news • Rich article metadata (featured entities) News Search API source: cnn.com
- 49. Accessing the APIs GEThttps://bingapis.azure-api.net/v5/search?q=nasa HTTP/1.1 OCP-Apim-Subscription-Key: <API KEY>
- 50.
- 51.
- 52.
- 54. Text Speech Image Dynamics365 On-premises IFTTT Speech Language understanding Translator QnA Maker Bing search Start State2 State 3 State 1 Bot framework SDK dialog manager Dispatch
- 56. Domain specific pretrainedmodels To reduce time to market LanguageSpeech … SearchVision Powerful infrastructure To accelerate deep learning CPU GPU FPGA PyCharm Jupyter Familiar Data Science tools To simplify model development Visual Studio Code Command line Popular frameworks To build advanced deep learning solutions TensorFlowPytorch OnnxScikit-Learn Azure Databricks Machine Learning VMs Azure Machine Learning Productive services To empower data science and development teams From the Intelligent Cloud to the Intelligent Edge
- 57. Azure AI Platform– Machine Learning Easily build, deploy, and share predictive analytics solutions • Simple, scalable, cutting edge. A fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions. • Deploy in minutes. Azure Machine Learning means business. You can deploy your model into production as a web service that can be called from any device, anywhere and that can use any data source. • Publish, share, monetize. Share your solution with the world in the Gallery or on the Azure Marketplace. Machine Learning and Analytics HDInsight (Hadoop and Spark) Stream Analytics Data Lake Analytics Machine Learning
- 58. A ZURE MACHINELEARNING STUDIO VISUAL DRAG-AND-DROP A ZURE MACHINE LEARNING SERVICES CODE-FIRST (TENSORFLOW, CNTK, ETC.) Azure AI Platform - Machine Learning for Engineers, Developers and Data Scientists
- 59. Vision Computer Vision |Video Indexer | Face | Content Moderator Speech … Speech to Text | Text to Speech | Speech Translation | Speaker Recognition Language Text Analytics | Spell Check | Language Understanding | Text Translation | QnA Maker Bing Search Big Web Search | Video Search | Image Search | Visual Search | Entity Search | News Search | Autosuggest
- 60. PyCharm Jupyter VisualStudio Code Command lineZeppelin Interactive widgets for Jupyter Notebooks Azure Machine Learning for Visual Studio Code extension
- 62. What is MachineLearning?
- 63. What is MachineLearning? Machine Learning is a process by which computers find patterns in data Makes those patterns available to applications. Application can then gain insights on new data based on conformity to the identified patterns.
- 64. Why Machine Learning? Enablesyou to learn from data faster than humans and make decisions on new events. Power of ML comes from the information that we can extract from existing data in the system. Enables to find patterns that are not obvious to humans. It can self adapt to new events.
- 65.
- 67. Microsoft & MachineLearning Bing maps launches What’s the best way to home? Kinect launches What does that motion “mean”? Azure Machine Learning What will happen next? Hotmail launches Which email is junk? Bing search launches Which searches are most relevant? Skype Translator launches What is that person saying? 201420091997 201520102008
- 68. Machine Learning Examples:Image Analyze
- 69. Machine Learning Examples:Which border color is the best?
- 70. Machine Learning Examples:Which border color is best?
- 71. Machine Learning Examples:Text Analytics - User reviews Positive Negative
- 72. What is MachineLearning?
- 73. Two main typesof Machine Learning problems
- 74.
- 75. Regression versus Classification Regressionproblems • Estimate household power consumption • Estimate customer’s income Classification problems • Power station will / will not meet demand • Customer will respond to advertising
- 76. Common Classes ofAlgorithms Classification Regression Anomaly Detection Clustering
- 77. Identifying to whichof a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Classification A set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors’). Regression The task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). Clustering The identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomaly Detection Common Classes of Algorithms
- 78. Supervised vs UnsupervisedLearning Unsupervised Learning • Unlabeled Data • Clustering (K-means) • Market Basket Analysis (Apriori Algorithm) • Anomaly Detection (Extreme Studentized Deviate Test) • Text mining (Latent Dirichlet Allocation) Supervised learning • Labeled Data • Anomaly Detection (Support Vector Machine) • Text mining (Support Vector Machine) • Regression (Linear Regression) • Classification (Random Forest) • Sentiment analysis (Neural Network)
- 79.
- 81.
- 82. 50°F 30°F 68°F95°F1990 48°F 29°F 70°F 98°F2000 49°F 27°F 67°F 96°F2010 ? ? ? ?2020 … … … …… Known data Model Unknown data Weather forecast sample Using known data, develop a model to predict unknown data.
- 83. 90°F -26°F 50°F 30°F 68°F95°F1990 48°F 29°F 70°F 98°F2000 49°F 27°F 67°F 96°F2010 Using known data, develop a model to predict unknown data. Predict 2020 Summer
- 85.
- 86. Binary versus MulticlassClassification Multiclass examples • kind of tree • kind of network attack • type of heart disease Binary examples • click prediction • yes|no • over|under • win|loss
- 87. Example of aClassification Model (Decision Tree) Age<30 Income > $50K Xbox-One Customer Not Xbox-One Customer Days Played > 728 Income > $50K Xbox-One Customer Not Xbox-One Customer Xbox-One Customer
- 88.
- 90. Unsupervised learning -Clustering
- 91.
- 92.
- 93.
- 95. Data Science Process 20%of work • Perform Statistical analysis • Discover and handle outliers • Prepare a list of predictive model techniques • Clearly defined business problem • Set success criteria • Define clear data science objectives • Break business problems to data science problems • Identify machine learning problem categories • Understand data points and constraints • Formulate data analytics strategy • Perform required transformation • Experiment with multiple models • Choose the optimal model • Create a feedback loop Define Business Problem Map to Machine Learning Problem Data Preparation Exploratory Data Analysis Modeling Evaluation 80% of work
- 96. Build a MachineLearning Solution Politics Sports Tech Health Using known data, develop a model to predict unknown data.
- 97. Build a MachineLearning Solution Using known data, develop a model to predict unknown data. Documents Labels Tech Health Politics Politics Sports Documents consist of unstructured text. Machine learning typically assumes a more structured format of examples
- 98. Build a MachineLearning Solution Using known data, develop a model to predict unknown data. LabelsDocuments Feature Documents Labels Tech Health Politics Politics Sports
- 99. Build a MachineLearning Solution Known data Data instance {40, (180, 82), (11,7), 70, …..} : Healthy Age Height/Weight Blood Pressure Hearth Rate LabelFeatures Feature Vector
- 100. Build a MachineLearning Solution Using known data, develop a model to predict unknown data. Documents Labels Tech Health Politics Politics Sports Training data Train the Model Feature Vectors Base Model Adjust Parameters
- 101. Build a MachineLearning Solution Known data with true labels Tech Health Politics Politics Sports Tech Health Politics Politics Sports Tech Health Politics Politics Sports Model’s Performance Difference between “True Labels” and “Predicted Labels” True labels Tech Health Politics Politics Sports Predicted labels Train the Model Split Detach +/- +/- +/-
- 102.
- 104. Microsoft Azure MachineLearning Studio
- 106. Overview of thelabs https://tinyurl.com/AMLS0001 https://tinyurl.com/AMLSLAB2 https://tinyurl.com/AMLS0003
- 107. Hands On Lab1: Predict Employee Leave (Classification) https://tinyurl.com/AMLS0001 https://www.datachangers.com/predict-employee-leave/
- 108. Hands On Lab2: Predict Annual Income (Classification) https://tinyurl.com/AMLSLAB2 https://www.datachangers.com/ai-workshop-predict-annual-income/
- 109.
- 110.
- 111.
- 112. Azure Notebooks –Azure Integration Your Azure Subscription is fully integrated into Azure Notebooks • Create a private notebook service for users of your Azure Subscription • Create custom Docker images to run your notebooks • Use premium computing resources to run your notebook (more cores, more RAM, GPU). • Always-free compute tier available Your Azure Subscription credentials available for single-sign-on • Use Azure data resources (e.g., SQL Azure, Cosmos DB, Azure Blob and Table storage) without needing to embed credentials in your notebooks • Use Azure compute resources (e.g., Azure Batch, Azure Batch AI, Azure Machine Learning) without needing to embed credentials in your notebooks
- 113. Quick Walkthrough ofAzure Notebooks https://notebooks.azure.com
- 114. Sign in andCreate an ID
- 115. Azure Notebooks -Projects Use this to: Create New Projects Upload OR Clone from GitHub Migrate (Upload) a local Jupyter notebook Share your Notebook
- 116. Azure Notebook -Editor View
- 117. Azure Notebooks –Toolbar and Shortcuts Mode What Shortcut Command (Press Esc to enter) Run cell Shift-Enter Command Add cell below B Command Add cell above A Command Delete a cell d-d Command Go into edit mode Enter Edit (Press Enter to enable) Run cell Shift-Enter Edit Indent Clrl-] Edit Unindent Ctrl-[ Edit Comment section Ctrl-/ Edit Function introspection Shift-Tab
- 118. Hands On Lab3: Predict Flight Delay (Classification) https://tinyurl.com/AMLS0003 https://github.com/Microsoft/computerscience/tree/master/Labs/Deep%20Learning/200 %20-%20Machine%20Learning%20in%20Python
- 119. Hands On Lab4: Bike Rental Challenge
- 120. If You HaveSome Time Left… https://github.com/Microsoft/computerscience/tree/master/Labs/Deep %20Learning
- 122. Microsoft Offer forStudents
- 123. Lab Tool Resources https://studio.azureml.net/ QuickStart Tutorial Tutorial Data Scientist Guide https://notebooks.azure.com/ Quick guide Tutorial Run Azure Notebook with Python Sample Notebooks
- 124. Other Azure MLExamples https://github.com/topics/azure-machine-learning https://github.com/Azure/LearnAnalytics- DoingMachineLearningwithAzureMLStudio/tree/master/instructor_resources Image clustering using Python and Azure Machine Learning Studio Workshop Azure ML Studio https://academy.datachangers.com/courses/course- v1:Microsoft+DAT275x+2018_T4/about https://github.com/MicrosoftLearning/Principles-of-Machine-Learning-Python
- 125. Other Azure MLExamples
- 126.