When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Machine Learning on Google Cloud
Build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models, create Vertex AI custom training jobs using Keras and TensorFlow, use Vertex AI Feature Store for data...
Read MoreBuild Vertex AI AutoML models without writing a single line of code; build BigQuery ML models, create Vertex AI custom training jobs using Keras and TensorFlow, use Vertex AI Feature Store for data management, use feature engineering for model improvement, determine the appropriate data preprocessing options for your use case, leverage best practices to implement machine learning on Google Cloud.
Skills Gained
This series of courses teaches participants the following skills:
- Build, train, and deploy an ML model by using Vertex AI AutoML.
- Understand when to use AutoML and BigQuery ML.
- Create Vertex AI-managed datasets.
- Add features to the Vertex AI Feature Store.
- Describe Analytics Hub, Dataplex, and Data Catalog.
- Describe how Vertex AI Vizier is used to improve model performance.
- Create a Vertex AI Workbench user-managed notebook, build a custom training job, and then deploy it by using
- a Docker container.
- Describe batch and online predictions and model monitoring.
- Describe how to improve data quality and explore your data.
- Build and train supervised learning models.
- Optimize and evaluate models by using loss functions and performance metrics.
- Create repeatable and scalable train, eval, and test datasets.
- Implement ML models by using TensorFlow or Keras.
- Understand the benefits of using feature engineering.
- Explain Vertex AI Model Monitoring and Vertex AI Pipelines.
Who Can Benefit
This class is intended for the following participants:
- Aspiring machine learning data analysts, data scientists, and data engineers
- Learners who want exposure to ML and use Vertex AI, AutoML, BigQuery ML, Vertex AI Feature Store, Vertex AI Workbench, Dataflow, Vertex AI Vizier for hyperparameter tuning, and TensorFlow/Keras
Prerequisites
To get the most out of this specialization, participants should have:
- Some familiarity with basic machine learning concepts
- Basic proficiency with a scripting language, preferably Python
Course Details
Course Outline
Launching into Machine Learning
- Describe how to improve data quality.
- Perform exploratory data analysis.
- Build and train supervised learning models.
- Describe AutoML and how to build, train, and deploy an ML model without writing a single line of code.
- Describe BigQuery ML and its benefits.
- Optimize and evaluate models by using loss functions and performance metrics.
- Mitigate common problems that arise in machine learning.
- Create repeatable and scalable training, evaluation, and test datasets.
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 that uses tf.keras preprocessing layers.
- Use the Keras Sequential and Functional APIs for simple and advanced model creation.
- Train, deploy, and productionalize ML models at scale with the Vertex AI Training Service.
Feature Engineering
- Describe Vertex AI Feature Store.
- Compare the key required aspects of a good feature.
- Use tf.keras.preprocessing utilities for working with image data, text data, and sequence data.
- Perform feature engineering by using BigQuery ML, Keras, and TensorFlow.
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, BigQuery ML, and custom training differ and when to use
- a particular framework.
- Describe hyperparameter tuning by using Vertex AI Vizier 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.
- Describe best practices for model deployment and serving, model monitoring, Vertex AI Pipelines, and artifact organization.
