Preparing for Google Cloud Certification: Machine Learning Engineer

Identify the data-to-AI lifecycle on Google Cloud and major products of big data and machine learning. Design streaming pipelines with Dataflow and Pub/Sub. Understand how to build machine learning solutions and create machine learning pipelines using AutoML.

Learn about Vertex AI Platform, best practices for implementing machine learning on Google Cloud, and Responsible AI best practices. Explore leveraging Google Cloud tools and environment for machine learning.

Improve data quality, perform exploratory data analysis, build and train AutoML models using Vertex AI and BigQuery ML, and create repeatable and scalable training, evaluation, and test datasets.

Create TensorFlow and Keras machine learning models, use the tf.data library for data manipulation, and train, deploy, and productionalize ML models at scale with Vertex AI.

Describe Vertex AI Feature Store, perform feature engineering using various tools, and preprocess and explore features with Dataflow and Dataprep. Utilize tf.Transform for feature engineering.

Understand data management, governance, and preprocessing options. Learn when to use Vertex AutoML, BigQuery ML, and custom training. Implement Vertex Vizier Hyperparameter Tuning and create batch and online predictions.

Compare static versus dynamic training and inference, manage model dependencies, set up distributed training, and export models for portability. Understand the aspects of production machine learning systems.

Identify core technologies required for MLOps, adopt best CI/CD practices, configure and provision Google Cloud architectures, and implement reliable training and inference workflows.

Learn about TensorFlow Extended (TFX) for ML pipeline management and orchestration, automation of pipelines through continuous integration and deployment, and ML pipeline reuse across multiple ML frameworks. Understand MLflow for managing the complete machine learning lifecycle.