This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
Overview
Syllabus
- Welcome to the Machine Learning Operations (MLOps): Getting Started
- Course introduction
- Employing Machine Learning Operations
- Introduction to MLOps-Why and when to employ MLOps
- Machine learning (ML) practitioners’ pain points
- The concept of devOps in ML
- ML lifecycle
- Automating the ML process
- Quiz
- Reading list
- Vertex AI and MLOps on Vertex AI
- What is vertex ai and why does a unified platform matter?
- Introduction to mlops on vertex ai
- How does vertex ai help with the mlops workflow, part 1?
- How does vertex ai help with the mlops workflow, part 2?
- Reading list
- Quiz
- Lab introduction Vertex AI: Qwik Start
- Training and Deploying a TensorFlow Model in Vertex AI
- Summary
- Summary
- All Readings
- Your Next Steps
- Course Badge
Tags
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Machine Learning Operations (MLOps): Getting Started
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Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
