UPDATE: Please note that this course will be upgraded to AI 102 with the new curriculum. This means that even if you are preparing for AI 100, you can continue to use this course for AI 102 preparation. -Microsoft Azure offers a spread of services designed to work together to enable rapid development of high-performance AI solutions. This skill teaches how these Azure services work together to enable you to design, implement, operationalize, monitor, optimize, and secure your AI solutions on Microsoft Azure. This path is designed to address the Microsoft AI-100 certification exam. This course covers Azure Cognitive APIs for Visual Features including Face Detection, Tagging the content of an image, OCR as well as Text Analytics for Language Detection, Sentiment Analysis and Key Phrase extraction. The course is very hands on and covers the implementation of these APIs using Python as well as Javascript. With cognitive services you will be able to build all such or even more types of applications. Here is the course content covered in this course: Analyze solution requirements (25-30%)Recommend Azure Cognitive Services APIs to meet business requirements select the processing architecture for a solution select the appropriate data processing technologies select the appropriate AI models and services identify components and technologies required to connect service endpoints identify automation requirements Map security requirements to tools, technologies, and processes identify processes and regulations needed to conform with data privacy, protection, and regulatory requirements identify which users and groups have access to information and interfaces identify appropriate tools for a solution identify auditing requirements Select the software, services, and storage required to support a solution identify appropriate services and tools for a solution identify integration points with other Microsoft services identify storage required to store logging, bot state data, and Azure Cognitive Services outputDesign AI solutions (40-45%)Design solutions that include one or more pipelines define an AI application workflow process design a strategy for ingest and egress data design the integration point between multiple workflows and pipelines design pipelines that use AI apps design pipelines that call Azure Machine Learning models select an AI solution that meet cost constraints Design solutions that uses Cognitive Services design solutions that use vision, speech, language, knowledge, search, and anomaly detection APIs Design solutions that implement the Microsoft Bot Framework integrate bots and AI solutions design bot services that use Language Understanding (LUIS) design bots that integrate with channels integrate bots with Azure app services and Azure Application Insights Design the compute infrastructure to support a solution identify whether to create a GPU, FPGA, or CPU-based solution identify whether to use a cloud-based, on-premises, or hybrid compute infrastructure select a compute solution that meets cost constraints Design for data governance, compliance, integrity, and security define how users and applications will authenticate to AI services design a content moderation strategy for data usage within an AI solution ensure that data adheres to compliance requirements defined by your organization ensure appropriate governance of data design strategies to ensure that the solution meets data privacy regulations and industry standards Implement and monitor AI solutions (25-30%) Implement an AI workflow develop AI pipelines manage the flow of data through the solution components implement data logging processes define and construct interfaces for custom AI services create solution endpoints develop streaming solutions Integrate AI services and solution components configure prerequisite components and input datasets to allow the consumption of Azure Cognitive Services APIs configure integration with Azure Cognitive Services configure prerequisite components to allow connectivity to the Microsoft Bot Framework implement Azure Cognitive Search in a solution Monitor and evaluate the AI environment identify the differences between KPIs, reported metrics, and root causes of the differences identify the differences between expected and actual workflow throughput maintain an AI solution for continuous improvement monitor AI components for availability recommend changes to an AI solution based on performance dataHope this course would be informative to you. Please reach out to me if you have any questions.
