MLOps Tools (MLflow, Kubeflow)
Build practical skills in mlops tools (mlflow, kubeflow) and apply key concepts, tools, and techniques to real-world business and technology scenarios.
Course Overview
Course Overview Module 1: Core Concepts • Introduction to MLOps Tools (MLflow, Kubeflow) • Hands-on with popular MLOps platforms • ML lifecycle and production concepts • Implementation and automation workflow Module 2: Tools & Techniques • ML lifecycle and production concepts • Implementation and automation workflow • Monitoring, reliability and governance • Practical tools and deployment scenarios Module 3: Practical Implementation • ML lifecycle and production concepts • Implementation and automation workflow • Monitoring, reliability and governance • Practical tools and deployment scenarios Module 4: Applied Skills • ML lifecycle and production concepts • Implementation and automation workflow • Monitoring, reliability and governance • Practical tools and deployment scenarios Module 5: Real-World Application • Monitoring, reliability and governance • Practical tools and deployment scenarios • Real-world business use cases • Best practices and key takeaways
Learning Outcomes
Understand the core concepts and terminology of MLOps Tools (MLflow, Kubeflow).
Apply relevant tools, techniques, and workflows in practical scenarios.
Analyze common problems and select appropriate approaches.
Work through real-world examples and business use cases.
Apply best practices to improve quality, efficiency, and outcomes.
Prerequisites
• Basic computer and internet skills. • Basic understanding of the relevant business or technology context. • Logical and analytical thinking. • Willingness to participate in practical exercises. • No prior experience in the specific course topic is required unless otherwise stated.
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