Describe the Vertex AI Platform and how it is used to quickly build, train, and deploy AutoML machine learning models without writing a single line of code.
Describe best practices for implementing machine learning on Google Cloud.
Develop a data strategy around machine learning
Examine use cases that are then reimagined through an ML lens
Leverage Google Cloud Platform tools and environment to do ML
Module 2: Launching into Machine Learning
Describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code.
Describe Big Query ML and its benefits.
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.
Mitigate common problems that arise in machine learning.
Create repeatable and scalable training, evaluation, and test datasets.
Module 3:TensorFlow on Google Cloud
Create TensorFlow and Keras machine learning models.
Describe TensorFlow key components.
Use the tf.data library to manipulate data and large datasets.
Build a ML model using tf.keras preprocessing layers.
Use the Keras Sequential and Functional APIs for simple and advanced model creation. Understand how model subclassing can be used for more customized models.
Module 4: Feature Engineering
Describe Vertex AI Feature Store.
Compare the key required aspects of a good feature.
Combine and create new feature combinations through feature crosses.
Perform feature engineering using BQML, Keras, and TensorFlow.
Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep.
Understand and apply how TensorFlow transforms features.
Module 5: Machine Learning in the Enterprise
Understand the tools required for data management and governance
Describe the best approach for data preprocessing - from providing an overview of DataFlow and DataPrep to using SQL for preprocessing tasks.
Explain how AutoML, BQML, and custom training differ and when to use a particular framework.
Describe hyperparameter tuning using Vertex Vizier and how it can be used to improve model performance.
Explain prediction and model monitoring and how Vertex AI can be used to manage ML models.
