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 Docker0; use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud. Learn all this and more!
Who should attend
- Aspiring machine learning data analysts, data scientists and data engineers
- Learners who want exposure to ML and use Vertex AI AutoML, BigQuery ML, Vertex AI Feature Store, Vertex AI Workbench, Dataflow, Vertex AI Vizier for hyperparameter tuning, TensorFlow/Keras.
- Some familiarity with basic machine learning concepts.
- Basic proficiency with a scripting language, preferably Python.
- Build, train, and deploy a machine learning model without writing a single line of code using Vertex AI AutoML.
- Understand when to use AutoML and Big Query ML.
- Create Vertex AI managed datasets.
- Add features to a Feature Store.
- Describe Analytics Hub, Dataplex, and Data Catalog.
- Describe hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance.
- Create a Vertex AI Workbench User-Managed Notebook, build a custom training job, and then deploy it using a Docker container.
- Describe batch and online predictions and model monitoring.
- Describe how to improve data quality.
- Perform exploratory data analysis.
- Build and train supervised learning models.
- Optimize and evaluate models using loss functions and performance metrics.
- Create repeatable and scalable train, eval, and test datasets.
- Implement ML models using TensorFlow/Keras.
- Describe how to represent and transform features.
- Understand the benefits of using feature engineering.
- Explain Vertex AI Pipelines.