Machine Learning on Google Cloud
About this course
What is machine learning, and what kinds of problems can it solve? How can you build, train, and deploy machine learning models at scale without writing a single line of code? When should you use automated machine learning or custom training? This course teaches you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker); use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; use Vertex Vizier hyperparameter tuning to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems, write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud. > By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <
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What you'll learn
- build Vertex AI AutoML models without coding
- create models using BigQuery ML with basic SQL
- deploy custom training jobs using containers
- manage data using Feature Store
- conduct feature engineering for model improvement
- determine appropriate data preprocessing options
- perform hyperparameter tuning with Vertex Vizier
- develop distributed ML models that scale using TensorFlow
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