Machine Learning on Google Cloud (MLGC)
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, 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 scientists and engineers.
- Learners who want exposure to ML using Vertex AI AutoML, BQML, Feature Store, Workbench, Dataflow, Vizier for hyperparameter tuning, TensorFlow/Keras.
- Some familiarity with basic machine learning concepts.
- Basic proficiency with a scripting language - Python preferred.
- 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, 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, 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
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