Learning plan for MLOps

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Courses are arranged from introductory to advanced. Durations reflect Coursera’s estimates (often assuming ~10 hours/week).

1. Orientation – understanding AI’s role in business

Introductory courses to frame AI/ML in business
Course (provider) Why it matters / key skills Approx. duration
AI for Everyone DeepLearning.AI Designed for non‑technical audiences; explains what AI can and cannot do, how to spot AI opportunities, how AI projects are built, how to work with AI teams, and ethical considerations . ≈6 hours (≈1 week)
Machine Learning Essentials for Business and Technical Decision Makers AWS Discusses benefits and risks of adopting ML, identifies data & production requirements, and explains how organizations must adapt to use ML responsibly . ≈1 hour
Solve Business Problems with AI and Machine Learning CertNexus How to identify business problems for AI/ML, formulate approaches, select appropriate tools, and address data privacy & ethics . 4 modules; ≈10 hours (≈1 week)
GenAI for Business Analysts: Faster InsightsCoursera Identify specific methods & techniques for leveraging GenAI to enhance business‑process modeling, requirements gathering, and stakeholder management. 1 module; ≈2 hours
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2. Machine Learning Operations Specilization

Get hands‑on with LLMs and automation
Course Why it matters Approx. duration
MLOps | Machine Learning Operations SpecializationDuke This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon. ≈150 hours
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