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End-to-End MLOps on Azure ML & MLflow

Master MLOps workflows using Azure Machine Learning and MLflow to operationalize, scale, and monitor ML pipelines end-to-end.
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Course Duration: 10 Hours
Preview End-to-End MLOps on Azure ML & MLflow course
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End-to-End MLOps on Azure ML & MLflow is a hands-on, industry-aligned course that teaches learners how to streamline and automate the full machine learning lifecycle using Microsoft Azure’s cloud-native tools and the popular MLflow platform. Designed for ML engineers, data scientists, and DevOps professionals, this course equips you with the knowledge and skills to productionize models efficiently, collaboratively, and at scale.
 
MLOps (Machine Learning Operations) brings DevOps best practices into the ML development process, solving challenges related to versioning, reproducibility, deployment, and monitoring. This course combines the enterprise capabilities of Azure Machine Learning with the flexibility and open-source nature of MLflow, providing a robust end-to-end approach for managing ML workflows in real-world environments.
 
What is End-to-End MLOps on Azure ML & MLflow?
 
This course focuses on implementing the full MLOps lifecycle—from data preparation and model training to CI/CD, model registry, and post-deployment monitoring—using Azure ML for scalable compute and MLflow for experiment tracking and model management. You'll learn to set up reproducible environments, automate pipelines, register and deploy models, and monitor them for performance drift, bias, and usage in production.
 
By combining the power of Azure ML pipelines, Azure DevOps, Kubernetes (AKS), and MLflow, you’ll be able to deliver secure, scalable, and transparent ML services in cloud environments.
 
How to Use This Course
 
To gain the most from this course:
  • Follow the modules in sequence, as each builds on the previous concepts.
  • Practice hands-on labs using the Azure ML studio and notebooks integrated with MLflow.
  • Use real-world datasets and simulate production scenarios including A/B testing, shadow deployments, and model retraining.
  • Engage in the capstone project to build a complete MLOps pipeline for a business case such as customer churn prediction or fraud detection.
  • Refer to Azure documentation and MLflow APIs for deeper understanding and extension of the tools.
This course prepares you to lead MLOps initiatives in organizations of all sizes by enabling you to automate and monitor your machine learning workflows effectively.
 
 

Course/Topic 1 - Coming Soon

  • The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.

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Course Objectives Back to Top
By the end of this course, you will be able to:
 
  1. Understand the fundamentals and need for MLOps in production environments.
  2. Set up Azure ML workspaces, compute targets, and environments.
  3. Train and track experiments using MLflow integrated with Azure ML.
  4. Design and run Azure ML pipelines with parameterized steps.
  5. Use MLflow for model versioning and reproducibility.
  6. Register and deploy models on Azure Kubernetes Service (AKS).
  7. Implement CI/CD for ML using Azure DevOps and GitHub Actions.
  8. Monitor models for performance drift, bias, and telemetry using Azure Monitor.
  9. Trigger automated retraining based on model degradation or new data.
  10. Build secure and auditable ML workflows with role-based access and logging.
Course Syllabus Back to Top
Course Syllabus
 
Module 1: Introduction to MLOps
  • What is MLOps and Why It Matters
  • Key Challenges in ML Lifecycle
  • Overview of Azure ML & MLflow Ecosystem
Module 2: Setting Up Azure ML Workspace
  • Creating an Azure ML Workspace
  • Compute Targets: Compute Instance vs Cluster
  • Datastores and Data Preparation
Module 3: Experiment Tracking with MLflow
  • MLflow Components: Tracking, Projects, Models, Registry
  • Logging Parameters, Metrics, and Artifacts
  • Integration of MLflow with Azure ML Notebooks
Module 4: Training ML Models on Azure ML
  • Environment Setup and Dependency Management
  • Submit Script Run Configuration
  • Custom Training Script Execution and Debugging
Module 5: Designing Azure ML Pipelines
  • Pipeline Architecture and Step Definitions
  • Data Input/Output and Parallel Processing
  • Pipeline Parameterization and Reuse
Module 6: Model Registration and Versioning
  • Registering Models in Azure ML and MLflow
  • Managing Model Lineage and Metadata
  • Comparing Model Versions for Promotion
Module 7: Model Deployment
  • Deploying Models to AKS, ACI, and Endpoints
  • Real-time and Batch Inference
  • Swagger and REST API Integration
Module 8: CI/CD for ML
  • MLOps with Azure DevOps and GitHub Actions
  • YAML Pipelines for Model Training and Deployment
  • Model Approval Gates and Automated Promotion
Module 9: Model Monitoring and Logging
  • Logging Custom Metrics to Azure Monitor
  • Detecting Data Drift and Performance Degradation
  • Setting Alerts for Model Behavior
Module 10: Responsible MLOps
  • Model Explainability and Fairness Checks
  • Role-Based Access Control (RBAC) in Azure ML
  • Audit Logging and Governance in ML Pipelines
Module 11: Real-World Deployment Patterns
  • Canary Deployments and Blue-Green Strategies
  • A/B Testing and Shadow Deployment
  • Scaling Inference with AKS Autoscaling
Module 12: Capstone Project
 
  • Build an End-to-End MLOps Pipeline
  • Use Case: Churn Prediction / Fraud Detection
  • Deploy, Monitor, and Retrain
Certification Back to Top
Upon successful completion, you will receive the Certificate of Completion in End-to-End MLOps on Azure ML & MLflow issued by Uplatz. This certificate validates your ability to design, build, and maintain production-grade ML pipelines using industry-standard tools. It demonstrates your understanding of both cloud-native infrastructure on Azure and open-source tracking/versioning with MLflow.
 
This certification is a mark of your operational expertise in machine learning, highlighting your proficiency in model automation, version control, deployment, and monitoring. It enhances your professional profile for roles in AI/ML engineering, DevOps, and data platform operations.
 
Employers value certified MLOps professionals who can bridge the gap between data science and production systems. Whether you're aiming to streamline ML delivery in a startup or architect enterprise-level workflows in a multinational company, this certificate showcases your readiness to own ML workflows from development to post-deployment monitoring.
Career & Jobs Back to Top
MLOps is one of the fastest-growing domains in the AI and cloud ecosystem. With the explosion of machine learning use cases, organizations require professionals who can move beyond experimentation and ensure stable, secure, and automated deployments of ML models.
 
This course positions you for a range of high-demand roles including:
  • MLOps Engineer
  • Machine Learning Engineer
  • DevOps Engineer (AI/ML Focus)
  • Cloud Data Engineer
  • Azure ML Solutions Architect
  • Data Science Platform Engineer
Professionals with MLOps skills are essential in data-driven organizations such as fintech firms, healthcare providers, e-commerce platforms, telecom companies, and cloud consultancies. You’ll be equipped to work in multidisciplinary teams bridging data science, engineering, and operations.
 
Cloud-native skills in Azure ML paired with the open-source versatility of MLflow are particularly in demand. Enterprises value this hybrid skillset for its scalability, reproducibility, and maintainability. As AI models become mission-critical, the ability to automate model testing, tracking, deployment, and monitoring becomes a necessity—not a luxury.
 
If you are looking to scale your ML projects, ensure compliance, or improve delivery speed, MLOps is the career path to choose—and this course provides the complete roadmap to get there.
Interview Questions Back to Top
1. What is MLOps and how does it differ from DevOps?
MLOps extends DevOps principles to the ML lifecycle, covering data preparation, model training, deployment, and monitoring, unlike DevOps which focuses on software CI/CD only.
 
2. What is Azure Machine Learning used for?
Azure ML is a cloud service for building, training, and deploying ML models using scalable compute, pipelines, and built-in tracking tools.
 
3. How does MLflow support experiment tracking?
MLflow lets you log and compare experiments by recording parameters, metrics, artifacts, and source code versions.
 
4. What are ML pipelines in Azure ML?
Pipelines orchestrate ML workflows as modular steps that can run sequentially or in parallel with defined dependencies and inputs.
 
5. What is the difference between ACI and AKS in Azure ML deployment?
ACI (Azure Container Instances) is suitable for testing, while AKS (Azure Kubernetes Service) is used for scalable production deployment.
 
6. How can you implement CI/CD for ML on Azure?
Use Azure DevOps or GitHub Actions to automate model training, testing, registration, and deployment via YAML pipelines and approval gates.
 
7. How do you monitor deployed models in Azure?
Use Azure Monitor and Application Insights to track model requests, latency, errors, drift, and custom metrics.
 
8. What is model drift?
Model drift refers to a decline in model performance over time due to changes in data distributions or real-world conditions.
 
9. How does MLflow manage model versions?
MLflow’s model registry allows you to register, version, stage, and deploy models, ensuring traceability and rollback.
 
10. What is a shadow deployment in MLOps?
Shadow deployment runs a new model in parallel with the production model, without affecting users, to test performance safely.
Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

Q3. How long is the course access?
A3. All our video courses comes with lifetime access. Once you purchase a video course with Uplatz you have lifetime access to the course i.e. forever. You can access your course any time via our website and/or mobile app and learn at your own convenience.

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A5. We do not take exam as part of the our training programs whether it is video course or live online class. These courses are professional courses and are offered to upskill and move on in the career ladder. However if there is an associated exam to the subject you are learning with us then you need to contact the relevant examination authority for booking your exam.

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A12. Career Path programs are our comprehensive learning package of video course. These are combined in a way by keeping in mind the career you would like to aim after doing career path program. Career path programs ranges from 100 hours to 600 hours and covers wide variety of courses for you to become an expert on those technologies. Check all Career Path Programs here https://training.uplatz.com/online-it-courses.php?career_path_courses=done

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A13. Learning Path programs are dedicated courses designed by SAP professionals to start and enhance their career in an SAP domain. It covers from basic to advance level of all courses across each business function. These programs are available across SAP finance, SAP Logistics, SAP HR, SAP succcessfactors, SAP Technical, SAP Sales, SAP S/4HANA and many more Check all Learning path here https://training.uplatz.com/online-it-courses.php?learning_path_courses=done

Q14. What are Premium Career tracks?
A14. Premium Career tracks are programs consisting of video courses that lead to skills required by C-suite executives such as CEO, CTO, CFO, and so on. These programs will help you gain knowledge and acumen to become a senior management executive.

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A15. Uplatz offers 2 types of unlimited subscription, Monthly and Yearly. Our monthly subscription give you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews each month. Minimum committment is for 1 year, you can cancel anytime after 1 year of enrolment. Our yearly subscription gives you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews every year. Minimum committment is for 1 year, you can cancel the plan anytime after 1 year. Check our monthly and yearly subscription here https://training.uplatz.com/online-it-courses.php?search=subscription

Q16. Do you provide software access with video course?
A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

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A20. Once you have made the payment on our website, you can access your course by clicking on the "My Courses" option in the main menu or by navigating to your profile, then the student dashboard, and finally selecting "Video Courses".

Q21. Can I get help from a tutor if I have doubts while learning from a video course?
A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.



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