Semantic Kernel SDK for Intelligent Applications from Coursera

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Unlock the full potential of AI-driven business solutions with the Semantic Kernel SDK. This course equips you with the knowledge to integrate Large Language Models (LLMs) and generative AI into your applications using Microsoft’s Semantic Kernel. You'll gain a deep understanding of how to create intelligent agents, build chat applications, and design plugins tailored to business needs.

The course begins with foundational concepts, including LLMs, generative AI, and the role of Semantic Kernel in modern applications. You’ll set up your development environment using tools like Visual Studio, VS Code, and Azure, preparing you for hands-on development. Next, you’ll dive into building your own Semantic Kernel, creating AI-powered chat applications, and configuring Azure OpenAI resources.

In the second half, you'll focus on extending the functionality of Semantic Kernel through built-in and custom plugins. You'll design career advisor tools, integrate personas, and manage prompts effectively. The course also covers native functions, their automation, and how to enhance interactions using function-prompt combinations.

This course is ideal for developers, software engineers, and tech-savvy business professionals interested in building AI-powered applications. A basic understanding of programming and familiarity with development environments like Visual Studio or Azure is recommended. The course is designed at an intermediate level.

What's inside

Syllabus

Introduction

In this module, we will introduce the Semantic Kernel SDK and establish its significance in the evolving landscape of AI integration. You'll get a clear understanding of what the course covers and how it empowers developers to build intelligent applications. This sets the stage for deeper technical exploration in subsequent modules.

Career center

Learners who complete Semantic Kernel SDK for Intelligent Applications will develop knowledge and skills that may be useful to these careers:

Generative Artificial Intelligence Developer

A Generative Artificial Intelligence Developer focuses on creating applications and systems that leverage generative AI models to produce new content, solve complex problems, or enhance user interactions. This cutting-edge role demands both AI expertise and development prowess. This course is specifically designed for individuals looking to become a Generative Artificial Intelligence Developer. It immerses learners in the practical application of generative AI and Large Language Models using Microsoft’s Semantic Kernel SDK. Through hands-on modules, participants will learn to build intelligent agents, create AI-powered chat applications, and extend functionality with custom plugins. The course’s emphasis on configuring Azure OpenAI resources, designing career advisor tools, and enhancing interactions using function-prompt combinations directly aligns with the responsibilities of this role. By developing a full-stack AI-powered chat assistant, learners gain comprehensive skills in bringing generative AI solutions to life, from backend to user interface.

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Chatbot Developer

A Chatbot Developer specializes in designing, building, and deploying conversational AI systems that can interact with users through text or voice. This role requires expertise in natural language processing and integration with backend services. This course is highly relevant for aspiring Chatbot Developers, as a significant portion is dedicated to building and enhancing AI-powered chat applications. Learners will gain hands-on experience creating a full-stack AI-powered chat assistant using the Semantic Kernel SDK, including implementing backend services, UI components, and managing chat history. The course covers crucial aspects like integrating Large Language Models and generative AI, designing custom plugins, managing prompts effectively, and leveraging techniques such as Retrieval Augmented Generation (RAG) to improve contextual accuracy. This comprehensive approach equips a Chatbot Developer with the practical skills to create sophisticated and intelligent conversational interfaces for myriad business needs.

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Software Development Engineer Artificial Intelligence

A Software Development Engineer Artificial Intelligence specializes in building software applications that incorporate AI functionalities, often leveraging large language models and generative AI. This pivotal role combines traditional software development with advanced AI integration. This course is exceptionally well-suited for a Software Development Engineer Artificial Intelligence, as it provides specific training on using the Semantic Kernel SDK to develop intelligent applications. Learners will gain hands-on experience setting up development environments with Visual Studio and Azure, building AI-powered chat applications, and designing custom plugins tailored to business needs. The curriculum's focus on creating AI agents, managing prompts, and automating functions directly translates into the practical skills required to develop sophisticated AI-enhanced software. Furthermore, the development of a full-stack AI-powered chat assistant, including backend services and UI components, equips learners with the end-to-end knowledge valued in this field.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer builds, deploys, and maintains AI systems, often integrating sophisticated models into production applications. This pivotal role requires a strong understanding of AI frameworks and deployment practices. This course directly addresses the core skills needed by an Artificial Intelligence Engineer by focusing on the practical application of Large Language Models and generative AI through the Semantic Kernel SDK. Learners will gain hands-on experience configuring development environments using Visual Studio and Azure, building intelligent applications, and creating AI-powered chat solutions. The ability to design custom plugins and manage prompts effectively, as taught in this course, is crucial for developing robust and adaptable AI solutions. Moreover, the course covers native functions and their automation, which is essential for developing interactive and intelligent agents. Someone aspiring to be an Artificial Intelligence Engineer will find this course helps establish a strong foundation for integrating cutting-edge AI technologies into real-world business solutions, preparing them for the demands of this dynamic field.

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Machine Learning Engineer

A Machine Learning Engineer focuses on designing, building, and deploying machine learning models and systems. This often involves working with various AI paradigms, including generative AI and large language models, to create intelligent applications. This course directly supports the skillset required for a Machine Learning Engineer by providing a deep dive into the Semantic Kernel SDK, a powerful tool for integrating advanced AI capabilities into applications. Learners will work with foundational AI concepts, set up development environments with Azure, and create AI-powered chat applications. The emphasis on developing plugins, integrating personas, and managing prompts for effective AI interaction is highly relevant. Additionally, features like creating native functions and implementing Retrieval Augmented Generation (RAG) for contextual accuracy are practical skills for a Machine Learning Engineer. This course helps learners gain practical experience in building and deploying intelligent agents, which is a core responsibility of this role.

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Application Developer AI Enhanced

An Application Developer AI Enhanced builds software applications that are significantly augmented by artificial intelligence capabilities, from basic integrations to complex intelligent agents. This role requires adapting traditional development skills to incorporate AI. This course is ideally suited for an Application Developer AI Enhanced, offering a direct pathway to integrating advanced AI into applications using the Semantic Kernel SDK. The curriculum provides hands-on experience in building intelligent agents, creating AI-powered chat applications, and designing custom plugins. Learners will set up development environments using Visual Studio and Azure, then dive into creating robust AI solutions. The course's focus on managing prompts, integrating native functions for automation, and developing full-stack AI assistants ensures that an Application Developer AI Enhanced gains the specific skills to embed sophisticated, generative AI features seamlessly into their applications, enhancing user experience and functionality.

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AI Tools Developer

An AI Tools Developer creates and maintains software tools, SDKs, and platforms that empower other developers to build and integrate artificial intelligence capabilities into their applications. This role requires a deep understanding of AI frameworks and developer experience. This course is highly relevant for an AI Tools Developer, as it focuses entirely on the Semantic Kernel SDK, a prime example of such a tool. By deeply understanding how to use, extend, and apply the Semantic Kernel for building intelligent agents and chat applications, learners gain invaluable insights into effective SDK design and functionality from a user's perspective. The modules on creating custom plugins, native functions, and managing prompt systems directly mirror the kind of work an AI Tools Developer might do to enhance an SDK's capabilities or build supporting libraries. This comprehensive, hands-on experience provides a unique perspective on what makes an AI development tool effective and user-friendly.

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Cloud Developer Azure

A Cloud Developer Azure specializes in building, deploying, and managing applications and services on Microsoft Azure. This role often involves leveraging Azure's extensive suite of services, including those for artificial intelligence. This course is highly relevant for a Cloud Developer Azure interested in integrating AI capabilities. It specifically guides learners through configuring their local environment with Azure integration, building necessary Azure resources, and configuring Azure OpenAI resources. The hands-on development of AI applications and a full-stack AI-powered chat assistant directly on Azure, utilizing its services, provides practical experience essential for this role. Understanding how to deploy and manage intelligent agents and generative AI solutions within the Azure ecosystem, as taught in this course, significantly enhances a Cloud Developer Azure’s ability to create cutting-edge, scalable cloud-native applications that leverage Microsoft's AI offerings.

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Natural Language Processing Engineer

A Natural Language Processing Engineer develops systems that enable computers to understand, interpret, and generate human language. This typically involves working with language models, text analysis, and conversational AI. This course may be helpful for a Natural Language Processing Engineer by providing a practical framework for applying Large Language Models and generative AI, which are central to modern NLP. While the course focuses on application development rather than core NLP algorithm design, it deeply engages with how these models are integrated through the Semantic Kernel SDK to create intelligent applications and chat assistants. Learners will explore prompt management, persona integration, and the use of native functions to enhance language interactions. The course's module on Retrieval Augmented Generation (RAG) for improving contextual accuracy is particularly relevant for an NLP Engineer focused on building robust and understanding language systems.

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Prompt Engineer

A Prompt Engineer specializes in crafting, refining, and optimizing prompts to guide large language models (LLMs) to perform specific tasks effectively and accurately. While often associated with textual inputs, this role also involves understanding model behavior and integration into larger systems. This course may be helpful for a Prompt Engineer by providing a comprehensive understanding of how LLMs and generative AI are integrated into applications using the Semantic Kernel SDK. Although the course delves into much broader application development, it explicitly covers managing prompts effectively and integrating personas to personalize responses. Learners will gain insights into how prompts interact within a larger intelligent agent framework and how they contribute to the functionality of AI-powered chat applications. This course could help a Prompt Engineer understand the architectural context of prompt usage and how their crafted prompts are utilized in real-world intelligent application development, offering a deeper technical perspective beyond just prompt creation.

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Artificial Intelligence Consultant

An Artificial Intelligence Consultant advises businesses on how to strategically adopt, implement, and optimize AI technologies to solve specific challenges and drive innovation. This role requires a strong grasp of AI capabilities, technical feasibility, and business impact. This course may be useful for an Artificial Intelligence Consultant by providing a deep, practical understanding of how to implement cutting-edge AI solutions using the Semantic Kernel SDK. By gaining hands-on experience with Large Language Models and generative AI, building intelligent agents, and creating AI-powered chat applications, learners will develop a robust technical foundation. The course's emphasis on designing plugins tailored to business needs, integrating personas, and understanding the business context view of AI agents directly informs the advice an AI Consultant would provide. This enables the consultant to speak credibly about implementation details and potential architectures, rather than just high-level concepts, which is invaluable for guiding clients effectively.

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Solutions Architect Artificial Intelligence

A Solutions Architect Artificial Intelligence designs and oversees the implementation of complex AI systems and solutions within an organization. This role requires a deep understanding of AI technologies, integration patterns, and cloud platforms to create scalable and robust architectures. This course may be useful for a Solutions Architect Artificial Intelligence by providing detailed, hands-on experience with a key integration framework, the Semantic Kernel SDK. Learners will explore the foundational concepts of LLMs and generative AI in a business context, set up development environments on Azure, and build functioning AI applications. The course's practical approach to creating intelligent agents, designing custom plugins, and implementing full-stack chat assistants provides valuable insights into the practical challenges and solutions in AI integration, which is essential for architects. Understanding how to integrate Azure OpenAI resources and apply techniques like RAG can directly inform architectural decisions for optimal performance and functionality.

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Technical Product Manager Artificial Intelligence

A Technical Product Manager Artificial Intelligence defines the strategy, roadmap, and features for AI-powered products, bridging the gap between technical teams and business objectives. This role demands both strong product acumen and a solid understanding of AI technologies. This course may be helpful for a Technical Product Manager Artificial Intelligence by offering a profound, practical immersion into building intelligent applications with the Semantic Kernel SDK. Understanding how Large Language Models and generative AI are integrated, from environment setup to deployment of full-stack chat assistants, is invaluable. The course covers designing plugins to meet business needs, integrating personas, and managing prompts effectively, all of which directly relate to product feature definition and user experience. This technical depth enables a Technical Product Manager Artificial Intelligence to make informed decisions, communicate effectively with engineering teams, and anticipate development challenges when bringing AI-driven products to market.

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Artificial Intelligence Research Scientist Applied

An Artificial Intelligence Research Scientist Applied typically focuses on translating cutting-edge AI research into practical applications and prototypes. This role often involves experimenting with new models, integration techniques, and deployment strategies for real-world impact. This course may be useful for an Artificial Intelligence Research Scientist Applied by providing a concrete framework, the Semantic Kernel SDK, for rapidly prototyping and implementing intelligent applications leveraging Large Language Models and generative AI. While not a theoretical research course, it offers deep practical insights into integrating AI models, creating custom plugins, and building full-stack AI-powered chat assistants. Understanding the practicalities of environment setup with Azure, prompt management, and RAG techniques is invaluable for applying research findings to functional systems. This course can help an Artificial Intelligence Research Scientist Applied to bridge the gap between theoretical knowledge and practical, deployable AI solutions. This role typically requires an advanced degree.

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Data Scientist Machine Learning Applications

A Data Scientist Machine Learning Applications focuses on developing and deploying machine learning models to solve specific business problems, often bridging the gap between data insights and functional software. This role requires both analytical skills and practical implementation capabilities. This course may be helpful for a Data Scientist Machine Learning Applications by providing a robust framework for integrating Large Language Models and generative AI into real-world applications using the Semantic Kernel SDK. While not centered on statistical modeling or pure data analysis, the course’s emphasis on building intelligent agents, creating AI-powered chat applications, and extending functionality with custom plugins offers crucial implementation skills. Learners will understand how to set up development environments, configure Azure OpenAI resources, and apply techniques like RAG to enhance contextual accuracy, greatly aiding in the deployment and operationalization of advanced AI capabilities. This course can help a Data Scientist Machine Learning Applications to transition from model development to building interactive, AI-driven solutions. This role typically requires an advanced degree.

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