Custom vision app,cognitive service ,aml designer quickview
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Custom vision app,cognitive service ,aml designer quickview
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Custom vision app,cognitive service ,aml designer quickview
- 9. Capability overview Azure Cognitive Services SearchVision Speech Intent: PlayCall Language
- 11. 電腦視覺/影像辨識
- 13. Visual Intelligence Made Easy Easily customize your own state-of-the-art computer vision models that fit to your unique use case. Just bring a few examples of labeled images and let Custom Vision do the hard work. Custom Vision Azure Cognitive Services
- 14. 電腦視覺/影像辨識
- 15. Custom Vision Azure Cognitive Services http://0rz.tw/C62kL
- 16. Custom Vision Azure Cognitive Services http://0rz.tw/kO1J6
- 18. Imagine, Create, Connect Azure Cognitive Services https://github.com/azure-samples/ https://aipartner.microsoft.com/directory#!/
- 19. Role of Cognitive Services making it easier to infuseAI frameworks TensorFlow KerasPytorch Onnx ML Extract intent Detect empty shelvesExtract identity effort pioneer expert developer everyone Pre-built, customizable services face OCR text vision speech translation QnA ...... use case featuresF1 F2 F3 F4 how deep in the stack? generic specialized technology
- 20. Video Indexer Azure Cognitive Services extract insights from Video and Audio files http://videoindexer.ai/
- 21. Video Indexer Azure Cognitive Services Capability Add-on Language identification Detect spoken language & support multi language content Linguistic Transcript Convert speech to text in 10 languages and allow extensions Captioning Create captions in three formats: vtt, ttml, srt Two channels processing Auto detect, balancing, separate transcript and merge to single timeline Noise reduction Clear up telephony audio or noisy recordings (based on Skype filters) Transcript customization Fit to Industry, market and domain specific terms Speaker enumeration Map and understand who spoke when Speaker statistics Statistics for speakers speech ratios in the audio Visual text recognition Extract and group text that appears in video as overlay, slides or background Keyword extraction Find out the keywords discussed in each segment Sentiment analysis Compare levels of positive vs negative spoken or written moments over the timeline Visual content moderation Detect explicit visuals such nudity and racy content Labels identification Tag objects such as cat, table, car, ball etc. when they appear Brand detection Track brand mentions in speech or on screen overheads with option to customize Celebrity identification Identify celebrities and see their biography Keyframe extraction Auto detection of stable keyframes in a movie Shot detection Detect when a shot starts/ends based on visual analysis Black frame detection Identification of black frames in a movie Audio effects Identify audio effects such as clapping, silence, speech Thumbnail extraction Automatically extract best face selection image Artifacts Rich next level of details via artifact files Inline editing Make manual fixes for errors detected Sub-clipping Source video is stored once for multiple playlists of video segments Search Understand the context of search results Widgets Easily embed delightful widgets of the insights and player Recommendations Find more videos with similar people discussing similar topics Custom face identification Customize face identification model Face detection Detect and group faces in the video Rest API Easily integrate with your application with REST API Text content moderation Detect explicit text in audio Translation immediate translate of source to 54 languages Topic inferencing Identify main topics of the video Emotions identification Detect emotions expressed in speech vocal signals
- 22. 影片Demo
- 23. Speech Azure Cognitive Services For optimal result in Speech to Text, customize: • acoustic models for your use environments, such as vehicles • field-specific vocabulary and grammar, such as medical or IT • pronunciation of abbreviations and acronyms, such as "IOU" for "I owe you." • Speaker Recognition • Speech to Text • Text to Speech
- 24. Language Understanding (LUIS) aims to be the most comprehensive cloud-based service for conversational understanding, and the easiest to use for developers with no AI expertise: • extracts intent/action and entities from user utterances • includes dictionaries, which can be extended with customer-specific terms Highlights: • now easier to integrate with Azure Bot, Speech and QnA Maker services Language Understanding Azure Cognitive Services
- 25. Q&A QnA Maker Azure Cognitive Services Microsoft & NDA Customers only—do not share
- 26. 影片Demo
- 27. Extract insights from customer feedback and social network postings. I had a wonderful trip to Seattle and enjoyed seeing the Space Needle! • submit up to 100 calls per minute, each with up to 1,000 documents of up to 5,000 characters each Links backed by Wikipedia in select languages Text Analytics Azure Cognitive Services
- 28. 影片Demo
- 29. translate dynamic content in your mobile, desktop and web apps automatically detect languages transliterate into different alphabets Text Translator – Translation Azure Cognitive Services 60+ supported languages: https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/language-support
- 30. 影片Demo
- 31. Resources: • Blog • Case Study • Documentation • Samples Content Moderator helps businesses manage risks associated with user generated content (UGC) by using machine-assisted content moderation APIs and a human review tool. Features include: Detection of potential adult, racy, and offensive, illegal, and unwanted image & video content Identification of possible profanity and undesirable text Built-in human review tool for improving the results Workflows that allow you to add other API’s and extract more content insights Customers using Content Moderator technologies include: Online marketplaces/e-Commerce sites for moderating catalogs and chatbots Social media/messaging/gaming platforms for moderating user content and digital assets Enterprises/K-12 solution providers moderating user content and information chatbots Global content moderation service providers using AI to augment human moderation teams Content Moderator Azure Cognitive Services
- 32. Bing Search Azure Cognitive Services
- 33. Personalization Azure Cognitive Services Personalization enables you to prioritize relevant content and user experiences, so they adapt to your audience’s preferences in a live, ongoing learning loop. Personalization uses the power of Reinforcement Learning to make the whole learning cycle at digital speed: it builds its own models, determines what experiments are worth trying, and learns from the real world with a simple reward feedback score that reflects your business goals. Personal Content Personal Interactions Personal Priorities 👍
- 34. 影片Demo
- 36. DevOps MLOps Code testing Code reproducibility App deployment Model retraining Model validation Model reproducibility Model deployment
- 37. App developer using Azure DevOps MLOps with Azure Machine Learning Build appCollaborate Test app Release app Monitor app Model reproducibility Model retrainingModel deploymentModel validation Data scientist using Azure Machine Learning
- 38. MLOps with Azure Machine Learning Code, dataset, and environment versioning Model reproducibility Model retrainingModel deploymentModel validation Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 39. MLOps with Azure Machine Learning Model validation & profiling Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 40. MLOps with Azure Machine Learning Model packaging Simple deployment across cloud and edge Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Deploy model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 41. MLOps with Azure Machine Learning Model management & monitoring Model performance analysis Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Deploy model Monitor model Retrain model Build appCollaborate Test app Release app Monitor app App developer using Azure DevOps Data scientist using Azure Machine Learning
- 42. MLOps with Azure Machine Learning Train model Validate model Deploy model Monitor model Retrain model Model reproducibility Model retrainingModel deploymentModel validation Build appCollaborate Test app Release app Monitor app Azure Machine Learning extension for Azure DevOps App developer using Azure DevOps Data scientist using Azure Machine Learning
- 43. MLOps with Azure Machine Learning Model reproducibility Model retrainingModel deploymentModel validation Train model Validate model Deploy model Monitor model Retrain model Build appCollaborate Test app Release app Monitor app Audit trail management and model interpretability App developer using Azure DevOps Data scientist using Azure Machine Learning
- 44. Steps to Build a Machine Learning Solution 1 Problem Framing 2 Get/Prepar e Data 3 Develop Model 4 Deploy Model 5 Evaluate / Track Performanc e 3.1 Analysis/ Metric definition 3.2 Feature Engineering 3.3 Model Training 3.4 Parameter Tuning 3.5 Evaluation
- 45. ● Create a model ○ Step 1: Get data ○ Step 2: Prepare the data ○ Step 3: Define features ● Train the model ○ Step 4: Choose and apply a learning algorithm ● Score and test the model ○ Step 5: Predict
- 46. Create a model Train the model Score and test the model