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AI-300 — MLOps Engineer

AI-300 — MLOps Engineer

Learn how to deploy, monitor, and manage machine learning and generative AI models in production using MLOps practices on Microsoft Azure.

₹15999

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Expiry period Lifetime
Made in English
Last updated at Thu May 2026
Level
Advanced
Total lectures 11
Total quizzes 0
Total duration 0
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Short description Learn how to deploy, monitor, and manage machine learning and generative AI models in production using MLOps practices on Microsoft Azure.
Outcomes
  • Understand the fundamentals of MLOps, Machine Learning lifecycle management, and enterprise AI deployment strategies.
  • Learn how to build automated CI/CD pipelines for Machine Learning models and AI applications in production environments.
  • Gain hands-on knowledge of model training, testing, deployment, version control, monitoring, and performance optimization workflows.
  • Explore cloud-based AI infrastructure, containerization, orchestration, and scalable deployment practices for modern AI systems.
  • Learn to implement automated model monitoring, drift detection, governance, security, and compliance for reliable AI operations.
  • Understand how to integrate DevOps, DataOps, and AI workflows to streamline end-to-end Machine Learning operations.
  • Develop industry-ready skills for careers in MLOps engineering, AI infrastructure management, cloud AI operations, and enterprise-scale AI automation.
Requirements
  • Basic understanding of Machine Learning and Artificial Intelligence concepts
  • Familiarity with programming languages such as Python is recommended
  • Knowledge of software development lifecycle, Git, or version control concepts is beneficial
  • Basic understanding of cloud computing platforms and DevOps practices is an advantage
  • Familiarity with data processing, APIs, databases, or automation workflows is helpful
  • Prior exposure to Machine Learning frameworks, containers, or CI/CD pipelines is beneficial but not mandatory