Structure

• Platform provided by Azure to build AI-based applications
• Azure Cognitive Services – Core Aspects
• Conversational AI
• Azure Computer Vision
• Use Cases – Enterprise scenarios
• Setting up developer tools

Objective

Azure AI platform

Azure Applied AI Services

• Azure Form Recognizer: It helps in identifying and extracting text, key/value pairs, and table data from form documents. It helps in ingesting text from forms and outputs structured data that includes the relationships in the original file.
• Azure Metrics Advisor: It helps in identifying and fixing problems leveraging a combination of near-real-time monitoring, adapting models to specific scenarios and helps with diagnostics and alerts.
• Azure Cognitive Search: It leverages built-in AI capabilities that help in identifying and exploring relevant content.
• Azure Immersive Reader: It is an inclusively designed tool that implements techniques to improve reading comprehension for language learners, new readers, and people with learning differences.
• Azure Video Analyser for Media: It helps extract metadata such as spoken words, faces etc from video and audio files.
• Azure Bot Service: It helps in developing conversational AI experiences.
• Azure Cognitive Services: Developers can rapidly consume high-level “finished” services that accelerate the development of AI solutions. They help in composing intelligent applications, customized to the organization’s availability, security, and compliance requirements.
• Azure Machine Learning: Consists of a set of comprehensive tools and frameworks that help build, deploy, and operationalize ML models. They provide an extensive set of supported tools and IDEs and Azure ML studio that help in easily building and fine-tuning ML models and deploying them.
• AI Infrastructure: AI Services and tools are backed by providing access to large scale infrastructure. It provides hyper-clusters of thousands of state-of-the-art GPUs, a breadth of AI hardware that includes the most comprehensive set of GPUs, and an array of general-purpose CPUs. It provides the latest high-bandwidth networks inside of every server.

AI Services

Trained Services (Prebuilt)

• Use cases for Trained/Prebuilt Services: The following are a few use cases for leveraging Cognitive/Trained services:

  

  Figure 1.4: Trained/Prebuilt/Cognitive Services
  • HealthCare: We can leverage OCR and Read APIs and other custom algorithms/logic as appropriate to go through large volumes of text that includes references to general entities (e.g. people’s names) and domain-specific ones (e.g. drug and disease names) that need to be connected and related. Sometimes we also need to combine this with imagery that’s analyzed in well-known ways (e.g. OCR) as well as apply leading-edge methods (e.g. AI-assisted diagnostics)
  • Oil and Gas companies: Oil and Gas companies have teams of geologists and other specialists that need to understand seismic and geologic data. They often have a huge library of PDFs with pictures of samples over sample sheets full of handwritten field notes. They need to connect places, people (domain experts), events, and navigate all this information to make key decisions. We can leverage OCR and other custom algorithms/logic to analyse and make decisions.
  • Insurance: Fraud Risk Analysis can be done by identifying anomalies in transactions using Azure Machine Learning, Face Recognition can be used as an additional authentication mechanism.

Conversational AI