Developing AI Applications on Azure

Go to Course: https://www.coursera.org/learn/developing-ai-applications-azure

Introduction

### Course Review: Developing AI Applications on Azure In today's rapidly evolving technological landscape, artificial intelligence (AI) and machine learning (ML) have become integral components across various sectors. For professionals looking to enhance their skills in this exciting field, the Coursera course **"Developing AI Applications on Azure"** stands out as a comprehensive and accessible option. This course not only introduces essential AI concepts but also provides hands-on experience with Microsoft Azure's powerful tools and resources for developing AI applications. #### Overview The **"Developing AI Applications on Azure"** course is expertly structured to walk learners through the fundamental concepts of AI and ML. It covers a variety of topics, starting from the basic definitions to complex applications using cloud-based tools. Leveraging Python—a primary programming language in the data science community—the course integrates scientific ecosystem packages that are indispensable for effective machine learning implementations. Whether you are a novice or have some background in programming, this course paves the way for understanding and applying AI principles practically. #### Syllabus Breakdown 1. **Introduction to Artificial Intelligence** - This module lays the foundation of AI and ML. It dives deep into machine learning types, tasks involved, and the algorithms powering them. You'll get to learn Python's role in ML solutions, exploring scientific packages that facilitate implementation. By the end, you will have acquired the skills to implement machine learning models using popular Python libraries. 2. **Standardized AI Processes and Azure Resources** - Here, the focus shifts to the tools available in Microsoft Azure. The course introduces you to standardized approaches for executing data analytics projects successfully, culminating with an overview of Microsoft's Team Data Science Approach. This module is particularly beneficial as it guides you through Azure's documentation to enhance your DevOps practices. 3. **Azure Cognitive APIs** - In this module, you will discover Microsoft's pre-trained machine learning APIs, commonly referred to as cognitive services. You will practically implement solutions using the Computer Vision API, Facial Recognition API, and Natural Language Processing for sentiment analysis. This hands-on experience will solidify your understanding of applying AI in real-world scenarios. 4. **Azure Machine Learning Service: Model Training** - This section provides insights into the Azure Machine Learning Service, guiding you to set up an ML workspace and train models. You will learn about the roles of experiments and runs, as well as the resources at your disposal for successful model training. Exercises include creating a workspace and executing training runs—necessities for any budding ML practitioner. 5. **Azure Machine Learning Service: Model Management and Deployment** - Finally, you'll learn about model management, covering how to connect to your workspace and utilize the model registry. This module wraps up the course by guiding you through the deployment process, which includes prepping your model for deployment, setting up a deployment target, and hosting it as a web service. You'll leave with the confidence to test your model using APIs. #### Recommendation I highly recommend the **"Developing AI Applications on Azure"** course for several reasons: 1. **Structured Learning Path**: The modular approach allows learners to grasp complex concepts in manageable chunks, progressing from foundational knowledge to advanced applications. 2. **Hands-on Experience**: Each module offers practical exercises, ensuring that you not only learn the theory but also implement it effectively using Azure's tools. 3. **Industry-Relevant Skills**: With AI and ML being prevalent in various industries, the skills gained from this course are highly desirable and can significantly enhance your career prospects. 4. **Community and Resources**: Access to Coursera's supportive community and abundant learning resources further enriches the learning experience, providing a platform for discussion and exploration. Whether you're aiming to break into the AI domain, upskill in your current role, or work on innovative projects, this course equips you with the necessary knowledge and skills to succeed. If you're passionate about harnessing the power of AI and machine learning with Azure's rich ecosystem, this course is a step in the right direction. Enroll today and unlock your potential in the world of Artificial Intelligence!

Syllabus

Introduction to Artificial Intelligence

This module introduces Artificial Intelligence and Machine learning. Next, we talk about machine learning types and tasks. This leads into a discussion of machine learning algorithms. Finally we explore python as a popular language for machine learning solutions and share some scientific ecosystem packages which will help you implement machine learning. By the end of this unit you will be able to implement machine learning models in at least one of the available python machine learning libraries.

This module introduces machine learning tools available in Microsoft Azure. It then looks at standardized approaches developed to help data analytics projects to be successful. Finally, it gives you specific guidance on Microsoft's Team Data Science Approach to include roles and tasks involved with the process. The exercise at the end of this unit points you to Microsoft's documentation to implement this process in their DevOps solution if you don't have your own.

This module introduces you to Microsoft's pretrained and managed machine learning offered as REST API's in their suite of cognitive services. We specifically implement solutions using the computer vision api, the facial recognition api, and do sentiment analysis by calling the natural language service.

This module introduces you to the capabilities of the Azure Machine Learning Service. We explore how to create and then reference an ML workspace. We then talk about how to train a machine learning model using the Azure ML service. We talk about the purpose and role of experiments, runs, and models. Finally, we talk about Azure resources available to train your machine learning models with. Exercises in this unit include creating a workspace, building a compute target, and executing a training run using the Azure ML service.

This module covers how to connect to your workspace. Next, we discuss how the model registry works and how to register a trained model locally and from a workspace training run. In addition, we show you the steps to prepare a model for deployment including identifying dependencies, configuring a deployment target, building a container image. Finally, we deploy a trained model as a webservice and test it by sending JSON objects to the API.