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⇱ Advanced Deployment, MLOps, and Generative AI in Azure | Coursera

Advanced Deployment, MLOps, and Generative AI in Azure

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Advanced Deployment, MLOps, and Generative AI in Azure

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9 hours to complete
Flexible schedule
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Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Master advanced deployment techniques for scaling and optimizing machine learning models.

  • Gain expertise in MLOps and learn to automate ML workflows with Azure DevOps and GitHub Actions.

  • Fine-tune generative AI models like GPT and DALL·E for real-world applications.

  • Learn to apply responsible AI practices to ensure fairness and transparency in AI models.

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Assessments

5 assignments

Taught in English

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This course is part of the Azure ML Bootcamp: Machine Learning on the Cloud Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will master advanced deployment strategies, MLOps, and generative AI using Azure ML Studio. You’ll explore techniques to scale machine learning workloads with parallel processing, distributed training, and serverless deployments, including deployment on edge devices and Kubernetes. Learn to manage machine learning workflows with Azure DevOps, GitHub Actions, and Infrastructure as Code (IaC), ensuring seamless integration and security. You’ll also dive into the fundamentals of generative AI, understanding how models like GPT, DALL·E, and others are revolutionizing the AI landscape, and how to fine-tune these models for specific tasks. Throughout the course, you’ll gain hands-on experience with real-time and batch inference, logging, and model monitoring using Azure Monitor and Application Insights. You will also work with cutting-edge tools to optimize models for inference speed and deploy them in production environments. The course will equip you with the skills to operationalize machine learning models effectively, from deployment to monitoring, ensuring they stay efficient and secure over time. This course is designed for professionals and developers looking to advance their skills in machine learning operations (MLOps) and explore the transformative potential of generative AI models. You will work with practical demos to apply what you learn in real-world scenarios, building deployable models that integrate seamlessly with your existing systems. By the end of the course, you will be able to deploy machine learning models using advanced strategies like distributed training and serverless deployment. Implement MLOps pipelines with Azure DevOps and GitHub Actions for end-to-end automation, and Fine-tune and optimize generative AI models like GPT and DALL·E for customized tasks.

In this module, we will dive into advanced strategies for deploying machine learning models on Azure. You’ll learn how to scale workloads using parallel processing and distributed training on Azure Compute Clusters. Additionally, we’ll explore deployment options like serverless solutions and real-time inference with Azure Kubernetes Service (AKS), along with securing your deployments and optimizing them for efficiency using ONNX.

What's included

12 videos2 readings1 assignment

12 videos•Total 135 minutes
  • Parallel Processing and Scaling ML Workloads•10 minutes
  • Distributed Training with TensorFlow/PyTorch on Azure Compute Clusters•9 minutes
  • DEMO - Distributed Training with TensorFlow/PyTorch on Azure Compute Clusters•10 minutes
  • Choosing Between Real-Time and Batch Inference•11 minutes
  • Serverless Model Deployments with Azure Functions•10 minutes
  • DEMO - Deploying a Real-Time ML Model on AKS•14 minutes
  • Role-Based Access Control (RBAC) and API Security•11 minutes
  • Logging and Alerting with Azure Monitor and Application Insights•10 minutes
  • DEMO - Logging and Alerting with Azure Monitor and Application Insights•21 minutes
  • Introduction to Advanced Deployment Strategies•11 minutes
  • Deploying ML Models on Edge Devices with Azure IoT•9 minutes
  • Model Optimization with ONNX for Efficient Inference•8 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'Advanced Deployment, MLOps, and Generative AI in Azure'•10 minutes
  • Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
  • Advanced Model Deployment Strategy - Assessment•15 minutes

In this module, we will explore the key concepts of MLOps and its vital role in the lifecycle of machine learning models. You will learn how to automate workflows, manage environments using IaC, and ensure compliance with security standards like GDPR and HIPAA. Additionally, we’ll dive into best practices for model governance and secure project management through role-based access control.

What's included

7 videos1 assignment

7 videos•Total 51 minutes
  • Importance of MLOps in Modern AI Applications•10 minutes
  • Key Differences Between DevOps and MLOps•5 minutes
  • Challenges in Operationalizing ML Models•6 minutes
  • Automating ML Workflows with Azure DevOps & GitHub Actions•7 minutes
  • Infrastructure as Code (IaC) for ML Environments•6 minutes
  • Data Encryption, Compliance (GDPR, HIPAA), and Security Best Practices•9 minutes
  • DEMO - Role-Based Access in Azure ML•9 minutes
1 assignment•Total 15 minutes
  • MLOps (Machine Learning Operations) - Assessment•15 minutes

In this module, we will introduce you to the world of generative AI and how to work with models like GPT and DALL·E within Azure ML Studio. You’ll gain hands-on experience with demos on text generation, AI-generated art, and creating custom chatbots. We’ll also focus on fine-tuning techniques, ethical challenges, and how to ensure fairness, transparency, and accountability in generative AI development.

What's included

14 videos1 reading3 assignments

14 videos•Total 173 minutes
  • Understanding Generative AI: What Is It?•10 minutes
  • Understanding Generative AI: Types of Generative Models•14 minutes
  • Understanding Generative AI: Popular Generative AI Models•5 minutes
  • Lab Demo: Using GPT in Azure ML-1•18 minutes
  • Lab Demo: Using GPT in Azure ML-2•13 minutes
  • Lab Demo: Generating AI-Generated Art with DALL·E•20 minutes
  • Fine-Tuning Generative AI Models; Why Fine-Tuning Is Needed•9 minutes
  • Techniques for Fine-Tuning GPT & Other Models•8 minutes
  • Lab Demo: Creating a Domain-Specific Chatbot•17 minutes
  • Lab Demo: Enhancing Text Generation for Custom Use Cases•20 minutes
  • Ethical Considerations in Generative AI: Challenges in Generative AI•11 minutes
  • Techniques for Responsible AI Development•10 minutes
  • Lab Demo: Auditing Bias in AI Models•9 minutes
  • Lab Demo: Using Explainable AI in Azure ML•9 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'Advanced Deployment, MLOps, and Generative AI in Azure'•10 minutes
3 assignments•Total 135 minutes
  • Exploring Generative AI with Azure ML Studio - Assessment•15 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•60 minutes

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Frequently asked questions

MLOps is a set of practices that combines machine learning and DevOps to streamline the process of deploying, monitoring, and maintaining machine learning models in production environments. It is highly relevant because it enables teams to continuously integrate, deliver, and monitor models, ensuring they remain effective and scalable over time. As machine learning becomes increasingly integrated into business operations, MLOps ensures that models are efficiently managed, reducing the risks associated with model drift, poor performance, and security vulnerabilities.

The "Advanced Deployment, MLOps, and Generative AI in Azure" course provides an in-depth exploration of advanced strategies for deploying machine learning models, managing machine learning operations (MLOps), and leveraging generative AI using Microsoft Azure. It covers topics such as scaling machine learning workloads, implementing serverless deployments, automating workflows with Azure DevOps, and securing AI operations. Additionally, the course introduces generative AI models, focusing on how to use and fine-tune models like GPT and DALL·E in Azure ML Studio for creating advanced AI-driven applications.

After completing this course, you will have the skills to deploy machine learning models using advanced strategies, including distributed training and serverless deployments. You will also be able to automate machine learning workflows, manage environments using Infrastructure as Code, and implement robust security practices for AI models. Additionally, you will gain hands-on experience with generative AI models and learn how to fine-tune them for specific tasks, enabling you to create powerful AI-driven applications such as chatbots and custom text generators.

This course assumes a foundational understanding of machine learning, cloud computing, and basic experience with Azure. A solid grasp of machine learning concepts, such as model training, evaluation, and deployment, as well as familiarity with tools like Azure ML Studio, is recommended. The course also covers advanced topics like MLOps and generative AI, so prior experience with DevOps practices and machine learning models will be beneficial.

This course is ideal for machine learning engineers, data scientists, and cloud practitioners who want to deepen their understanding of advanced deployment strategies, MLOps, and generative AI in Azure. It is especially suited for professionals looking to work with AI models in production environments, automate machine learning workflows, and implement scalable and secure AI solutions in the cloud.

The course is designed to take approximately eight hours to complete, covering video lectures, demos, and practical hands-on exercises. Depending on your pace and experience level, it may take additional time to fully explore the concepts and practice the skills demonstrated in the course.

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,