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Hands-On MLOps Fundamentals for ML Engineers
Coursera · KodeKloud · Updated March 2026
Platform rating
4.4/5
AI Tutor Rating
8.2/10
Duration
1-3 months
Classes
60
Learn MLOps with hands-on experience using Apache Airflow, Kafka, Spark, and CI/CD pipelines for model deployment.
What you'll get
Build end-to-end ML pipelines with CI/CD
Orchestrate workflows with Apache Airflow
Deploy and monitor ML models in production
Fit
Best for
Software Engineers
DevOps/MLOps Engineers
Data Engineers
Platform Engineers
Not ideal for
Complete beginners
Non-technical learners
Prerequisites & pricing
Prerequisites
Basic Python, ML fundamentals
Pricing
Subscription
Certification
Certificate
Growth Leverage: Completing this course equips learners for roles such as MLOps Engineer, Data Engineer, or Machine Learning Engineer, enabling them to effectively manage and deploy ML models in production. It also prepares candidates for certifications like Google Professional Machine Learning Engineer and AWS Certified Machine Learning - Specialty.
Skills Value: The skills gained, including CI/CD implementation and workflow orchestration with Apache Airflow, meet the high demand for MLOps professionals, with salaries often exceeding $120,000 annually, as organizations seek to streamline ML development and deployment processes.
MLOps
Apache Airflow
Kafka
CI/CD
Data Pipelines