Make UofT - Using Azure Custom Vision from PoC to Enterprise
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Slides used during the event MakeUofT on Feb 16 2019, around Azure Custom Vision, Machine Learning and Docker Containers
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Make UofT - Using Azure Custom Vision from PoC to Enterprise
- 1. @elbruno How a PoC at home can scale to Enterprise Level using Custom Vision APIs Bruno Capuano Innovation Lead @Avanade @elbruno | http://elbruno.com
- 2. @elbruno why should I care about AI and ML? As a developer,
- 3. @elbruno Some problems are difficult to solve using traditional algorithms and procedural programming.
- 4. @elbruno IBM slaps patent on coffee-delivering drones that can read your MIND (link)
- 5. @elbruno IBM slaps patent on coffee-delivering drones that can read your MIND (link)
- 6. @elbruno IBM coffee-delivering drones test footage
- 7. @elbruno Microsoft AI platform Azure AI Services Azure Infrastructure Tools
- 8. @elbruno
- 9. @elbruno Play-offs de ascenso de LaLiga 1|2|3 2016-2017 Getafe 3 â 1 Tenerife (24 de junio de 2017) { "tags":[ "person", "grass", "soccer", "outdoor", "field", "playing", "game", "ball", "player", "sport", "man", "young", "group", "rugby", "people", "watching", "match", "crowd", "girl", "team", "uniform", "standing", "holding", "blue" ], "captions":[ { "text":"a group of people watching a football ball", "confidence":0.9469714 } ] } [ { "name":"person", "confidence":0.9998956 }, { "name":"grass", "confidence":0.9998543 }, { "name":"soccer", "confidence":0.992252052 }, { "name":"outdoor", "confidence":0.982327938 }, { "name":"field", "confidence":0.958654761 }, { "name":"athletic game", "confidence":0.938705862 }, { "name":"player", "confidence":0.9206911 }, { "name":"rugby", "confidence":0.459357172 } ] [ { "age":37, "gender":"Male", "faceRectangle":{ "top":51, "left":498, "width":54, "height":54 } } ] Computer Vision API
- 10. @elbruno Custom models Easier / Less Control Harder / Full Control Pre-built models TensorFlow ML.NETVisionSpeech LanguageKnowledge Search Artificial Intelligence Models
- 11. @elbruno Prepare Your Data Build & Train Run Build your own custom machine learning models
- 12. @elbruno
- 14. @elbruno Artificial Intelligence: Image Analysis
- 15. ONNX and WinML
- 16. @elbruno Windows ML uses ONNX models
- 17. @elbruno 1. Developers can focus on their data and their scenarios, using Windows ML for model evaluation 2. Enables using ML models trained with a diverse set of toolkits 3. Hardware acceleration gets fast evaluation results across the diversity of the entire Windows device ecosystem. Windows ML solves three problems for you Direct3D GPU CPU DirectML Model Inference Engine WinML Win32 API WinML UWP API Win32 App WinML Runtime UWP App
- 18. @elbruno Azure Machine Learning Services gives you an end-to-end solution to prepare data and train your model in the Cloud. WinMLTools converts existing models from CoreML, scikit- learn, LIBSVM, and XGBoost Azure Custom Vision makes it easy to create your own image models - https://customvision.ai/ Azure AI Gallery curates models for use with Windows ML - https://gallery.azure.ai/models How do I get ONNX models to use in my application?
- 19. @elbruno Custom Vision in Windows 10 using WinML and ONNX MakeMagicHappen(); https://www.avanade.com/AI
- 20. @elbruno Train Stations Parking Spots available Waiting Lines Objects in a counter And more âŚ. Use Cases #NoTrain #Train #NoTrain #NoTrain #NoTrain #NoTrain #Train #NoTrain #Train #Train
- 21. @elbruno Custom Vision Container in Raspberry Pi 3 MakeMagicHappen(); https://www.avanade.com/AI
- 22. @elbruno Donât be scare of AI Being a dev/programmer give you 80% of your required skills Containers are hard, but necessary ⢠You donât need to be a Data Scientist to create amazing solutions ⢠The other 20% is fun (or pain) ⢠Tears will come, thatâs for sure Lessons learned
- 23. @elbruno Bruno Capuano Innovation Lead @Avanade @elbruno | http://elbruno.com Q&A Thanks!
Editor's Notes
- Everyone in the room is a Machine Learning developer! ML is everywhere and chances are you are already using a service that is possible through ML. We are going to show you how AI and ML can help you solve real problems in your own apps and services and how easy it is to get started with ML today in your own apps
- Any technology user today has benefitted from machine learning. Facial recognition technology allows social media platforms to help users tag and share photos of friends. Recommendation engines, powered by machine learning, suggest what movies or television shows to watch next based on user preferences. Manufacturing processes contain sensors that collect vast amounts of data and are able to predict failures and react in real time. Self-driving cars that rely on machine learning to navigate are already being tested and may soon be available to consumers. In our projects, we are often asked to solve very hard problems. These three problems have something in common â they are relatively easy for a human to solve, but are difficult to program a computer to do. This is especially true, when the only tool available for us is procedural code and traditional algorithms. This is where AI can help!
- https://www.theregister.co.uk/2018/08/23/ibm_coffee_drone_patent/ https://dronelife.com/2018/08/24/ibm-patents-coffee-delivering-drone/
- https://www.theregister.co.uk/2018/08/23/ibm_coffee_drone_patent/ https://dronelife.com/2018/08/24/ibm-patents-coffee-delivering-drone/
- https://www.youtube.com/watch?v=suFH2VEQ9Wo
- 10
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- https://www.pexels.com/photo/dog-chihuahua-sobel-50718/
- NOTE TO PRESENTERS This deck contains a library of slides you can use to build a customized introduction to Avanade for external audiences, including clients, partners and recruits. The deck is not meant to be used in its entirety, but rather to provide options for how you introduce Avanade, allowing you to match your content to your audience and their interests. The slides at the front of the deck provide an overview of how Avanade sees the marketplace changing and how weâre helping clients respond to those changes. Your introduction can be extended with slides from the appendices, depending on your purposes, to provide additional information of specific relevance to your presentation audience.
- Industry standard format for Machine Learning model interchange A community project created by Microsoft and Facebook Defines an extensible computation graph, built-in operators, and standard data types. Supports a wide range of machine learning models including Classical and Deep Learning algorithms Learn more at http://onnx.ai/
- Let me explain how Windows ML solved 3 problems for me when building an application like the one I just showed you Windows ML provides an evaluation runtime, so you donât have to worry about finding one and deploying it with your application. Just focus on identifying your question, gathering your data â and let Windows ML provide the platform. Likewise, Windows ML provides me with APIs that I can use in either my Win32 application, or my UWP application Windows ML supports ONNX, the industry standard for Machine Learning model interop. No matter what training framework you used to make your model, as long as you can save or convert it into ONNX â you can evaluate it with Windows ML. On Windows, your evaluation code is HW accelerated using DX12. DirectX as you know, has a proven history of providing excellent performance across a wide range of hardware. This means that you donât have to change your code when running on different devices. This is a huge advantage compared to other platforms that depend on 3rd party device SDKs for evaluation acceleration. Weâve also optimized the CPU paths, using the instruction optimizations up to AVX-512.