Covers the design, construction, commissioning, optimization, operation and maintenance of machine learning systems.
The detailed learning content includes:
·Basics of machine learning: Covering the basic principles and methods of machine learning.
Feature engineering: Explore how to effectively process and transform data to improve model performance.
Production-level machine learning systems: Introduce how to deploy machine learning models into a production environment.
Computer vision and natural language processing: involving the analysis and application of image and language data.
·Recommendation system: Discuss how to build a personalized recommendation engine.
· MLOps: Focus on the practice of machine learning operations, including deployment, monitoring and maintenance of models.
TensorFlow, Google Cloud, VertexAI: Introduce how these tools and platforms support the development and deployment of machine learning projects.
Machine learning engineers design, build, produce, optimize, operate and maintain machine learning systems.
This learning path will guide you through a select collection of on-demand courses, laboratories, and skills badges, providing you with real-world hands-on experience using the Google Cloud technology necessary for your ML engineer role. After completing this path, check out the Google Cloud Machine Learning Engineer Certification to take the next steps in your career journey.
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Website:https://www.cloudskillsboost.google/paths/17
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