How long does it take to complete the course?

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.

Can I preview a course before enrolling?

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.

When will I have access to the lectures and assignments?

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.

What will I get when I enroll?

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.

When will I receive my 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.

Why can’t I audit this course?

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

Is financial aid 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.

Advanced Deployment, MLOps, and Generative AI in Azure

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Advanced Deployment, MLOps, and Generative AI in Azure

Ce cours fait partie de Spécialisation Azure ML Bootcamp: Machine Learning on the Cloud

Instructeur : Packt - Course Instructors

Inclus avec Coursera Plus

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Avancées

Expérience recommandée

9 heures à compléter

Planning flexible

Apprenez à votre propre rythme

3 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Avancées

Expérience recommandée

9 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : CI/CD
Catégorie : Real Time Data

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Récemment mis à jour !

octobre 2025

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5 devoirs

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Ce cours fait partie de la Spécialisation Azure ML Bootcamp: Machine Learning on the Cloud

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Il y a 3 modules dans ce cours

This course features Coursera Coach!

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.

Inclus

12 vidéos2 lectures1 devoir1 plugin

12 vidéosTotal 135 minutes

Parallel Processing and Scaling ML Workloads10 minutes
Distributed Training with TensorFlow/PyTorch on Azure Compute Clusters9 minutes
DEMO - Distributed Training with TensorFlow/PyTorch on Azure Compute Clusters10 minutes
Choosing Between Real-Time and Batch Inference11 minutes
Serverless Model Deployments with Azure Functions10 minutes
DEMO - Deploying a Real-Time ML Model on AKS13 minutes
Role-Based Access Control (RBAC) and API Security10 minutes
Logging and Alerting with Azure Monitor and Application Insights10 minutes
DEMO - Logging and Alerting with Azure Monitor and Application Insights21 minutes
Introduction to Advanced Deployment Strategies10 minutes
Deploying ML Models on Edge Devices with Azure IoT9 minutes
Model Optimization with ONNX for Efficient Inference8 minutes

2 lecturesTotal 20 minutes

Introduction to the Course 'Advanced Deployment, MLOps, and Generative AI in Azure'10 minutes
Full Specialization Resources10 minutes

1 devoirTotal 15 minutes

Advanced Model Deployment Strategy - Assessment15 minutes

1 pluginTotal 15 minutes

Logging and Monitoring ML Deployments on Azure15 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.

Inclus

7 vidéos1 devoir1 plugin

7 vidéosTotal 50 minutes

Importance of MLOps in Modern AI Applications10 minutes
Key Differences Between DevOps and MLOps4 minutes
Challenges in Operationalizing ML Models6 minutes
Automating ML Workflows with Azure DevOps & GitHub Actions6 minutes
Infrastructure as Code (IaC) for ML Environments5 minutes
Data Encryption, Compliance (GDPR, HIPAA), and Security Best Practices8 minutes
DEMO - Role-Based Access in Azure ML8 minutes

1 devoirTotal 15 minutes

MLOps (Machine Learning Operations) - Assessment15 minutes

1 pluginTotal 15 minutes

Understanding and Applying Role-Based Access Control (RBAC) in Azure15 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.

Inclus

14 vidéos1 lecture3 devoirs1 plugin

14 vidéosTotal 173 minutes

Understanding Generative AI: What Is It?10 minutes
Understanding Generative AI: Types of Generative Models13 minutes
Understanding Generative AI: Popular Generative AI Models4 minutes
Lab Demo: Using GPT in Azure ML-117 minutes
Lab Demo: Using GPT in Azure ML-212 minutes
Lab Demo: Generating AI-Generated Art with DALL·E19 minutes
Fine-Tuning Generative AI Models; Why Fine-Tuning Is Needed9 minutes
Techniques for Fine-Tuning GPT & Other Models8 minutes
Lab Demo: Creating a Domain-Specific Chatbot17 minutes
Lab Demo: Enhancing Text Generation for Custom Use Cases20 minutes
Ethical Considerations in Generative AI: Challenges in Generative AI11 minutes
Techniques for Responsible AI Development10 minutes
Lab Demo: Auditing Bias in AI Models9 minutes
Lab Demo: Using Explainable AI in Azure ML8 minutes

1 lectureTotal 10 minutes

Conclusion to the Course 'Advanced Deployment, MLOps, and Generative AI in Azure'10 minutes

3 devoirsTotal 135 minutes

Exploring Generative AI with Azure ML Studio - Assessment15 minutes
Full Course Assessment60 minutes
Full Course Practice Assessment60 minutes

1 pluginTotal 15 minutes

Understanding and Applying Role-Based Access Control (RBAC) in Azure15 minutes

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Instructeur

Packt - Course Instructors

Packt

1 155 Cours270 009 apprenants

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Packt

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Foire Aux 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.