Machine Learning on Google Cloud (MLGC)
Module 1: How Google Does Machine Learning
- 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.
- Describe the benefits of Vertex AI Pipelines