Microsoft Course: End-to-end machine learning operations (MLOps) with Azure Machine Learning
Automate, Deploy, Succeed: Transform ML Ideas into Production.
- Automate ML workflows using Azure jobs and GitHub Actions
- Implement CI/CD pipelines for model training, testing, and deployment
- Improve code quality with linting, unit testing, and environment management
- Streamline collaboration through feature-based development and branch protection
- Deploy models efficiently to production with Azure Machine Learning CLI (v2)
- Build a base for specialized roles in AI development or cloud engineering
- Prepare for more advanced Microsoft certifications in AI
What you will learn
This learning path, End-to-End Machine Learning Operations (MLOps) with Azure Machine Learning, teaches you to apply DevOps principles to machine learning projects. Over six modules and 3.5 hours, you'll master automation, CI/CD pipelines, and source control using Azure Machine Learning and GitHub Actions. Designed for Python/R developers with ML experience, the course guides you from model experimentation to production deployment, emphasizing scalable, repeatable workflows.
Microsoft's End-to-end machine learning operations (MLOps) with Azure Machine Learning course Outline
Learn the End-to-end machine learning operations (MLOps) with Azure Machine Learning
Looking to streamline your machine learning workflows and deploy models faster? Check out Microsoft's End-to-End MLOps with Azure Machine Learning course. This hands-on learning path teaches you to automate training, testing, and deployment using Azure ML and GitHub Actions—ideal for developers aiming to adopt DevOps best practices in ML. Learn how to reduce errors, ensure reproducibility, and scale your projects efficiently. Ready to level up your MLOps skills? Explore the course here and start building production-ready solutions today!
Start your journey today:
Course Structure Includes:
The course includes six modules:
- Automation with Azure ML Jobs: Transition models from experimentation to production.
- Trigger ML Jobs via GitHub Actions: Automate workflows using event-driven actions.
- Feature-Based Development: Protect branches and trigger tasks based on code changes.
- Linting & Unit Testing: Automate code validation for ML workloads.
- Environment Management: Train, test, and deploy models using versioned environments.
- Model Deployment Automation: Deploy and test models with GitHub Actions and Azure ML CLI.
Training Options
Self Paced Learning
- Lifelong access to high-quality content
- Curated by industry experts
- Customized learning progress
- 24/7 learner assistance and support
- Follow the latest technology trends
Exam Dump
- 100% Real Exam Practice Tests
- 100% Verified Exam Questions & Answers
- 100% Guarantee Passing Rate
- Average 7 Days to Practice & Pass
Description
Learn to automate ML workflows using Azure ML and GitHub Actions. Build CI/CD pipelines, deploy models faster, and implement DevOps best practices for machine learning.
Pre-requisites
Programming experience with Python or R
Experience developing and training machine learning models
Familiarity with basic Azure Machine Learning concepts
SPOTO Empowers You to Earn Your Certification.
Benefits of AWS Certified AI Practitioner Certification
Microsoft Certified: Azure AI Fundamentals (AI-900) is an entry-level certification designed to validate foundational knowledge of AI and machine learning concepts, including computer vision, natural language processing (NLP), generative AI, and responsible AI principles. Ideal for both technical and non-technical professionals, it requires no prior coding experience and offers free, flexible learning resources via Microsoft Learn. The certification enhances career prospects in high-demand roles like AI Engineer or Data Scientist, with potential salary increases up to 20%, while serving as a gateway to advanced Azure certifications (e.g., AI-102). Cost-effective and time-efficient (preparation in 10–30 hours), the exam focuses on real-world applications using Azure Cognitive Services, OpenAI APIs, and Azure Machine Learning. With permanent validity and global recognition across 90+ countries, it equips professionals to drive AI innovation in industries such as healthcare and finance. Start your AI journey with Microsoft Learn today!
Advantages of Using SPOTO's Exam Preparation Materials
SPOTO's study resources provide comprehensive coverage of the exam syllabus, aligning with key topics like ML problem framing, data processing, model optimization, and pipeline automation. The materials include structured learning paths, practice exams, and real-world case studies that mirror the certification's focus on designing, implementing, and deploying machine learning solutions with Microsoft services and ML workflows. By leveraging SPOTO's targeted content, candidates can efficiently bridge knowledge gaps, reinforce practical skills, and gain familiarity with the exam format. This focused preparation increases confidence and readiness, particularly for complex tasks such as deploying CI/CD pipelines or optimizing model performance—areas emphasized in the certification. Combined with hands-on experience, SPOTO's resources offer a strategic advantage for achieving certification success.
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