Describe how to improve data quality and perform exploratory data analysis

Build and train AutoML Models using Vertex AI and BigQuery ML

Optimize and evaluate models using loss functions and performance metrics

Create repeatable and scalable training, evaluation, and test datasets

Design and build a TensorFlow input data pipeline.

Use the tf.data library to manipulate data in large datasets.

Use the Keras Sequential and Functional APIs for simple and advanced model creation.

Train, deploy, and productionalize ML models at scale with Vertex AI.

Describe Vertex AI Feature Store and compare the key required aspects of a good feature.

Perform feature engineering using BigQuery ML, Keras, and TensorFlow.

Discuss how to preprocess and explore features with Dataflow and Dataprep.

Use tf.Transform.

Describe data management, governance, and preprocessing options

Identify when to use Vertex AutoML, BigQuery ML, and custom training

Implement Vertex Vizier Hyperparameter Tuning

Explain how to create batch and online predictions, setup model monitoring, and create pipelines using Vertex AI

Compare static versus dynamic training and inference

Manage model dependencies

Set up distributed training for fault tolerance, replication, and more

Export models for portability

Identify and use core technologies required to support effective MLOps.

Adopt the best CI/CD practices in the context of ML systems.

Configure and provision Google Cloud architectures for reliable and effective MLOps environments.

Implement reliable and repeatable training and inference workflows.

Develop a high level understanding of TFX standard pipeline components.

Learn how to use a TFX Interactive Context for prototype development of TFX pipelines.

Continuous Training with TensorFlow, PyTorch, XGBoost, and Scikit Learn Models with KubeFlow and AI Platform Pipelines

Perform continuous training with Composer and MLFlow