This course is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, Microsoft Bot Framework and Azure Open AI services. The course will use C#, Python, or JavaScript as the programming language.
This course has been updated with Azure Open AI Services to take advantage of large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. We will apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. The training will also explore how to detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security.
Software engineers concerned with building, managing, and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, Microsoft Bot Framework and Azure Open AI services. They are familiar with C#, Python, or JavaScript and have knowledge on using REST-based APIs.
By attending this course you will gain the following skills:
Module 1: Create and Manage Azure Cognitive Services
Prior to accessing any of the Cognitive Services functionality on Azure, you will need to create a Cognitive Services resource. Using the various services (Speech, Computer Vision etc.), requires, at a minimum, an access key and a service endpoint URL. The information is required for authorization of applications that will be accessing these services. You will create either a single-service resource or a multi-service resource, depending on the services you access.
Module 2: Implement Computer Vision Solutions
Learn how to integrate visual AI in your applications using Azure Computer Vision. Detect and identify faces or objects in images and video, perform object detection, classify images, and implement custom vision solutions.
Module 3: Implement Language Analysis Solutions
Learn how to implement natural language functionality in your applications through integration of the Language Understanding service. Gain insights into your users' intentions through text analytics features such as sentiment analysis and language detection. Identify important information in text files with entity and key phrase extraction capabilities.
Module 4: Implement Knowledge Mining Solutions
Azure Cognitive Search provides a cloud-based solution for indexing and querying a wide range of data sources and creating comprehensive and high-scale search solutions. Lean to implement a solution in which the documents are indexed and made easy to search.
Module 5: Implement Conversational AI Solutions
Use the Microsoft Bot Framework and the Bot Framework Composer to design and create conversational AI solutions.
Module 6: Implement Azure Open AI Services
We will explore how Azure OpenAI Service provides access to OpenAI's powerful language models including GPT-4, GPT-35-Turbo, Embeddings model series and others. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users will learn how to access the service through REST APIs, Python SDK, or the web-based interface in the Azure OpenAI Studio.
Module 7: Prompt engineering
In this module you will learn different techniques for prompt engineering and prompt flow for pre-trained models like GPT.
We will explore models like GPT-3.5, and GPT-4 from OpenAI that are prompt-based. With prompt-based models, the user interacts with the model by entering a text prompt, to which the model responds with a text completion. This completion is the model’s continuation of the input text.
While these models are extremely powerful, their behaviour is also very sensitive to the prompt. This makes prompt construction an important skill to develop.
We will explore Azure Machine Learning prompt flow. Azure Machine Learning prompt flow is a development tool designed to streamline the entire development cycle of AI applications powered by Large Language Models (LLMs). As the momentum for LLM-based AI applications continues to grow across the globe, Azure Machine Learning prompt flow provides a comprehensive solution that simplifies the process of prototyping, experimenting, iterating, and deploying your AI applications.
Module 8: Fine-tuning and working with your own data
In this module you will learn different techniques for customizing the pre-trained models like GPT for fine-tuning, embedding, working with your own data and using content filters for detecting and preventing the output of harmful content.
You will learn how to customize the pre-trained models through REST APIs, Python SDK, or the web-based interface in the Azure OpenAI Studio.
Azure OpenAI on your data enables you to run supported chat models such as GPT-35-Turbo and GPT-4 on your data. Running models on your data enables you to chat on top of and analyse your data with greater accuracy and speed. By doing so, you can unlock valuable insights that can help you make better business decisions, identify trends and patterns, and optimize your operations. One of the key benefits of Azure OpenAI on your data is its ability to tailor the content of conversational AI.
Read full, official course description:
AI-102: Designing and Implementing a Microsoft Azure AI Solution
This course will help you prepare for exam AI-102: Designing and Implementing an Microsoft Azure AI Solution
If you pass exam AI-102 you will get the certification:
Microsoft Certified: Azure AI Engineer Associate
