MLGCGOGoogleGO-MLGC3.5<ul> <li>Describe the technologies, products, and tools to build an ML model, an ML pipeline, and a Generative AI project.</li><li>Understand when to use AutoML and BigQuery ML.</li><li>Create Vertex AI-managed datasets.</li><li>Add features to the Vertex AI Feature Store.</li><li>Describe Analytics Hub, Dataplex, and Data Catalog.</li><li>Describe how to improve model performance.</li><li>Create Vertex AI Workbench user-managed notebook, build a custom training job, and deploy it by using a Docker container.</li><li>Describe batch and online predictions and model monitoring.</li><li>Describe how to improve data quality and explore your data.</li><li>Build and train supervised learning models.</li><li>Optimize and evaluate models by using loss functions and performance metrics.</li><li>Create repeatable and scalable train, eval, and test datasets.</li><li>Implement ML models by using TensorFlow or Keras.</li><li>Understand the benefits of using feature engineering.</li><li>Explain Vertex AI Model Monitoring and Vertex AI Pipelines.</li></ul><ul> <li>Some familiarity with basic machine learning concepts.</li><li>Basic proficiency with a scripting language, preferably Python.</li></ul><ul> <li>Aspiring machine learning data analysts, data scientists and data engineers</li><li>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, TensorFlow/Keras.</li></ul><h4>Module 1 - Introduction to AI and Machine Learning on Google Cloud</h4><p> <strong>Objectives:</strong> </p> <ul> <li>Recognize the AI/ML framework on Google Cloud.</li><li>Identify the major components of Google Cloud infrastructure.</li><li>Define the data and ML products on Google Cloud and how they support the datato-AI lifecycle.</li><li>Build an ML model with BigQueryML to bring data to AI.</li><li>Define different options to build an ML model on Google Cloud.</li><li>Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training.</li><li>Use the Natural Language API to analyze text.</li><li>Define the workflow of building an ML model.</li><li>Describe MLOps and workflow automation on Google Cloud.</li><li>Build an ML model from end-to-end by using AutoML on Vertex AI.</li><li>Define generative AI and large language models.</li><li>Use generative AI capabilities in AI development.</li><li>Recognize the AI solutions and the embedded generative AI features.</li></ul><p> <strong>Activities:</strong> </p> <ul> <li>Hands-On Labs</li><li>Module Quizzes</li><li>Module Readings</li></ul><h4>Module 2 - Launching into Machine Learning</h4><p> <strong>Objectives:</strong> </p> <ul> <li>Describe how to improve data quality.</li><li>Perform exploratory data analysis.</li><li>Build and train supervised learning models.</li><li>Describe AutoML and how to build, train, and deploy an ML model without writing a single line of code.</li><li>Describe BigQuery ML and its benefits.</li><li>Optimize and evaluate models by using loss functions and performance metrics.</li><li>Mitigate common problems that arise in machine learning.</li><li>Create repeatable and scalable training, evaluation, and test datasets.</li></ul><p> <strong>Activities:</strong> </p> <ul> <li>Hands-On Labs</li><li>Module Quizzes</li><li>Module Readings</li></ul><h4>Module 3 - TensorFlow on Google Cloud</h4><p> <strong>Objectives:</strong> </p> <ul> <li>Create TensorFlow and Keras machine learning models.</li><li>Describe the TensorFlow main components.</li><li>Use the tf.data library to manipulate data and large datasets.</li><li>Build a ML model that uses tf.keras preprocessing layers.</li><li>Use the Keras Sequential and Functional APIs for simple and advanced model creation.</li><li>Train, deploy, and productionalize ML models at scale with the Vertex AI Training Service.</li></ul><p> <strong>Activities:</strong> </p> <ul> <li>Hands-On Labs</li><li>Module Quizzes</li><li>Module Readings</li></ul><h4>Module 4 - Feature Engineering</h4><p> <strong>Objectives:</strong> </p> <ul> <li>Describe Vertex AI Feature Store.</li><li>Compare the key required aspects of a good feature.</li><li>Use tf.keras.preprocessing utilities for working with image data, text data, and sequence data.</li><li>Perform feature engineering by using BigQuery ML, Keras, and TensorFlow.</li></ul><p> <strong>Activities:</strong> </p> <ul> <li>Hands-On Labs</li><li>Module Quizzes</li><li>Module Readings</li></ul><h4>Module 5 - Machine Learning in the Enterprise</h4><p> <strong>Objectives:</strong>Understand the tools required for data management and governance. </p> <ul> <li>Describe the best approach for data preprocessing: From providing an overview of Dataflow and Dataprep to using SQL for preprocessing tasks.</li><li>Explain how AutoML, BigQuery ML, and custom training differ and when to use a particular framework.</li><li>Describe hyperparameter tuning by using Vertex AI Vizier to improve model performance.</li><li>Explain prediction and model monitoring and how Vertex AI can be used to manage ML models.</li><li>Describe the benefits of Vertex AI Pipelines.</li><li>Describe best practices for model deployment and serving, model monitoring, Vertex AI Pipelines, and artifact organization.</li></ul><p> <strong>Activities:</strong> </p> <ul> <li>Hands-On Labs</li><li>Module Quizzes</li><li>Module Readings</li></ul>- Describe the technologies, products, and tools to build an ML model, an ML pipeline, and a Generative AI project. - 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 to improve model performance. - Create Vertex AI Workbench user-managed notebook, build a custom training job, and 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.- Some familiarity with basic machine learning concepts. - Basic proficiency with a scripting language, preferably Python.- 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, TensorFlow/Keras.Module 1 - Introduction to AI and Machine Learning on Google Cloud Objectives: - Recognize the AI/ML framework on Google Cloud. - Identify the major components of Google Cloud infrastructure. - Define the data and ML products on Google Cloud and how they support the datato-AI lifecycle. - Build an ML model with BigQueryML to bring data to AI. - Define different options to build an ML model on Google Cloud. - Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training. - Use the Natural Language API to analyze text. - Define the workflow of building an ML model. - Describe MLOps and workflow automation on Google Cloud. - Build an ML model from end-to-end by using AutoML on Vertex AI. - Define generative AI and large language models. - Use generative AI capabilities in AI development. - Recognize the AI solutions and the embedded generative AI features. Activities: - Hands-On Labs - Module Quizzes - Module Readings Module 2 - Launching into Machine Learning Objectives: - 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. Activities: - Hands-On Labs - Module Quizzes - Module Readings Module 3 - TensorFlow on Google Cloud Objectives: - Create TensorFlow and Keras machine learning models. - Describe the TensorFlow main 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. Activities: - Hands-On Labs - Module Quizzes - Module Readings Module 4 - Feature Engineering Objectives: - 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. Activities: - Hands-On Labs - Module Quizzes - Module Readings Module 5 - Machine Learning in the Enterprise Objectives: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. Activities: - Hands-On Labs - Module Quizzes - Module Readings5 days3250.002995.003190.003250.0011270.002995.002995.003385.003385.003385.003250.004140.003250.003300.004135.003770.00