دانلود کتاب Practical Guide to Azure Cognitive Services: Leverage the power of Azure OpenAI to optimize operations, reduce costs, and deliver cutting-edge AI solutions

فهرست مطالب :

Cover\nTitle Page\nCopyright and Credits\nContributors\nTable of Contents\nPreface\nPart 1: Ocean Smart – an AI Success Story\nChapter 1: How Azure AI Changed Ocean Smart\n Choosing Azure Cognitive Services\n The Ocean Smart story\n Dealing with paperwork using Knowledge Mining\n Using Form Recognizer to process financial documents\n Using Anomaly Detector for discovering abnormalities\n Using Computer Vision to detect product quality issues early\n Applying Content Moderator to avoid the posting of inappropriate material\n Using the Personalizer service for product recommendations\n Applying Speech services for call center improvements\n Building your case: calculating ROI and TCO\n Building your justification\n Proving out your solution\n Summary\nChapter 2: Why Azure Cognitive Services?\n Exploring the history of Azure Cognitive Services\n Exploring Azure Cognitive Services\n Decision\n Speech\n Language\n Vision\n Reviewing past and future investments\n Accessibility\n Sustainability\n Operational efficiencies\n Summary\nChapter 3: Architectural and Cost Optimization Considerations\n Exploring core Azure services and costs\n Storage accounts\n Azure Virtual Network (VNet)\n Network Security Groups\n Databases\n Azure App Service\n Azure Functions\n Estimating the costs for a proof-of-concept solution, including all services\n Understanding data orchestration for loading data into Azure\n Summary\nPart 2: Deploying Next-Generation Knowledge Mining Solutions with Azure Cognitive Search\nChapter 4: Deriving Value from Knowledge Mining Solutions in Azure\n Reviewing a brief history of document collection solutions\n Understanding the drawbacks of traditional data collection systems\n Exploring the purpose and benefits of knowledge mining solutions\n Using cognitive services to develop knowledge mining solutions\n Summary\n Further reading\nChapter 5: Azure Cognitive Search Overview and Implementation\n Technical requirements\n Understanding how Azure Cognitive Search is built\n Search types\n Underlying index and search activities\n Exploring what services will be used as part of a Cognitive Search solution\n Pairing common services with Cognitive Search for your KM solution\n Summary\nChapter 6: Exploring Further Azure Cognitive Services for Successful KM Solutions\n Technical requirements\n Exploring native AI enrichments in Azure Cognitive Search\n Reviewing advancements in OCR and image recognition\n Understanding OCR enhancements\n Exploring image recognition enhancements\n Considering other cognitive services commonly used in KM\n Skills arrays\n Custom skills\n Adding additional skills to your Cognitive Search indexer\n Summary\nChapter 7: Pulling It All Together for a Complete KM Solution\n Technical requirements\n Getting your Azure environment set up to support your KM solution\n Deploying the Azure Cognitive Search service\n Azure storage account deployment\n Deploying Cognitive Search for your KM solution\n Ingesting your data for indexing and related activities\n Azure Speech-to-Text for audio transcription\n Loading files in Form Recognizer for processing\n Bulk loading of everything else\n Connecting to and deploying related services\n Batch processing audio files with Cognitive Speech\n Deploying the KM solution with monitoring and notification considerations\n Showing the code\n Summary\nPart 3: Other Cognitive Services That Will Help Your Company Optimize Operations\nChapter 8: Decluttering Paperwork with Form Recognizer\n Technical requirements\n Understanding Form Recognizer machine learning model options\n Building custom models\n Using Form Recognizer Development Studio\n Exploring the AP process at Ocean Smart\n Deploying Form Recognizer for mostly automating the AP process\n Creating Azure resources\n Deploying Azure App Service\n Integrating the complete solution for production use\n Building the demonstration\n Example code\n Summary\nChapter 9: Identifying Problems with Anomaly Detector\n Technical requirements\n Overview of the Anomaly Detector service\n Univariate Anomaly Detector\n Multivariate Anomaly Detector\n Using the Anomaly Detector algorithms with your data\n Async API\n Sync API\n Configuring and refining monitoring of data in your environment\n Defining and detecting anomalies in your organization\n Building the complete solution in Azure\n The SimulateChillerEvents function\n The ProcessChillerEvents function\n The Azure Stream Analytics job\n Summary\nChapter 10: Streamlining the Quality Control Process with Custom Vision\n Technical requirements\n Understanding the quality control process at Ocean Smart\n Evolving the quality control process with Custom Vision\n Training and improving the model\n Creating your first project\n Uploading and tagging images\n Model training\n Model performance\n Using Smart Labeler for faster image labeling\n Sending a notification of an irregular product\n Building the complete Custom Vision solution\n The processNewImage function\n The TrainNewModel function\n The PublishLatestIteration function\n Deploying the functions\n Using the Ocean Smart portal\n Summary\nChapter 11: Deploying a Content Moderator\n Technical requirements\n Overview of the Content Moderator service\n Pricing considerations when deploying the service\n Applying the service to text moderation\n Using a custom text list\n Applying the service to image moderation\n Applying the service to video moderation\n Flagging and reviewing inappropriate content\n Building the complete solution\n Summary\nChapter 12: Using Personalizer to Cater to Your Audience\n Technical requirements\n The Personalizer service and its origins\n Receiving and scoring consumer feedback\n Using Reinforcement Learning for recommendations\n Configuring Content, Actions, and Context to make recommendations\n Using evaluations to increase the effectiveness of your Personalized Service\n Building a completed solution\n Data used for this example\n RewardAction\n Testing and deploying the functions\n Using a test script\n Summary\nChapter 13: Improving Customer Experience with Speech to Text\n Technical requirements\n Overview of Azure Speech services\n Working with real-time audio and batch speech-to-text data\n Real-time speech-to-text\n Batch speech-to-text\n Improving speech-to-text accuracy with Custom Speech\n Working with different languages\n Using the translation services\n Building a complete batch solution\n TranscribeAudio function\n Retrieve transcription results Logic Apps app\n Failed transcription result\n Successful transcription results\n Capturing sentiment and reporting\n Summary\nChapter 14: Using Language Services in Chat Bots and Beyond\n Technical requirements\n Using a chat bot for enhanced customer service\n Tying the Cognitive Service for Language to the chat bot for NLP\n Using the translator services for worldwide solutions\n Advanced capabilities of Language Understanding\n Orchestration workflow\n Question answering\n None intents\n Building a bot using language Understanding\n Creating our bot project\n A quick tour of Bot Framework Composer\n Adding the check my order flow\n Allowing a user to add shipping notes – language translation\n Summary\nChapter 15: Surveying Our Progress\n Highlighting recent advancements in AI\n The lessons learned and returns on investments\n Industry ripples\n Evaluating future opportunities for optimization\n The ethics of AI\n Summary\nChapter 16: Appendix – Azure OpenAI Overview\n Reviewing industry trends\n Understanding what Azure OpenAI services are\n Clarifying common misconceptions\n Summary\nIndex\nAbout Packt\nOther Books You May Enjoy