Linkedin Learning – MLOps: Fundamentals of CI/CD and Model Deployment 2026
English | Tutorial | Size: 552.42 MB
As machine learning becomes central to modern software systems, DevOps engineers need new skills to manage models in production. This course introduces machine learning operations (MLOps) and covers how it extends DevOps practices to include data science workflows. Get an overview of the MLOps lifecycle-from CI/CD and continuous training to monitoring and governance-to learn how DataOps, ModelOps, and DevOps work together.
Find out how to collect and prepare data using tools like Pandas, Apache Spark, and Apache Kafka, then explore feature stores and pipeline orchestration with Airflow and Prefect. You get hands-on with MLflow for experiment tracking and model management, and BentoML for model deployment and serving. Along the way, learn about monitoring with Prometheus, Grafana, and Evidently, and how to address common data privacy, security, and compliance issues with GDPR, HIPAA, and PCI standards.
Note: This course was created by KodeKloud. We are pleased to host this training in our library.
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