{"course":{"productid":23770,"modality":1,"active":true,"language":"en","title":"Machine Learning on Google Cloud","productcode":"MLGC","vendorcode":"GO","vendorname":"Google","fullproductcode":"GO-MLGC","courseware":{"has_ekit":false,"has_printkit":true,"language":""},"url":"https:\/\/portal.flane.ch\/course\/google-mlgc","objective":"<ul>\n<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>","essentials":"<ul>\n<li>Some familiarity with basic machine learning concepts.<\/li><li>Basic proficiency with a scripting language, preferably Python.<\/li><\/ul>","audience":"<ul>\n<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>","outline":"<h4>Module 1 - Introduction to AI and Machine Learning on Google Cloud<\/h4><p>\n<strong>Objectives:<\/strong>\n<\/p>\n<ul>\n<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>\n<strong>Activities:<\/strong>\n<\/p>\n<ul>\n<li>Hands-On Labs<\/li><li>Module Quizzes<\/li><li>Module Readings<\/li><\/ul><h4>Module 2 - Launching into Machine Learning<\/h4><p>\n<strong>Objectives:<\/strong>\n<\/p>\n<ul>\n<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>\n<strong>Activities:<\/strong>\n<\/p>\n<ul>\n<li>Hands-On Labs<\/li><li>Module Quizzes<\/li><li>Module Readings<\/li><\/ul><h4>Module 3 - TensorFlow on Google Cloud<\/h4><p>\n<strong>Objectives:<\/strong>\n<\/p>\n<ul>\n<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>\n<strong>Activities:<\/strong>\n<\/p>\n<ul>\n<li>Hands-On Labs<\/li><li>Module Quizzes<\/li><li>Module Readings<\/li><\/ul><h4>Module 4 - Feature Engineering<\/h4><p>\n<strong>Objectives:<\/strong>\n<\/p>\n<ul>\n<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>\n<strong>Activities:<\/strong>\n<\/p>\n<ul>\n<li>Hands-On Labs<\/li><li>Module Quizzes<\/li><li>Module Readings<\/li><\/ul><h4>Module 5 - Machine Learning in the Enterprise<\/h4><p>\n<strong>Objectives:<\/strong>Understand the tools required for data management and governance.\n<\/p>\n<ul>\n<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>\n<strong>Activities:<\/strong>\n<\/p>\n<ul>\n<li>Hands-On Labs<\/li><li>Module Quizzes<\/li><li>Module Readings<\/li><\/ul>","summary":"<p>This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques and feature engineering.<\/p>","objective_plain":"- Describe the technologies, products, and tools to build an ML model, an ML pipeline, and a Generative AI project.\n- Understand when to use AutoML and BigQuery ML.\n- Create Vertex AI-managed datasets.\n- Add features to the Vertex AI Feature Store.\n- Describe Analytics Hub, Dataplex, and Data Catalog.\n- Describe how to improve model performance.\n- Create Vertex AI Workbench user-managed notebook, build a custom training job, and deploy it by using a Docker container.\n- Describe batch and online predictions and model monitoring.\n- Describe how to improve data quality and explore your data.\n- Build and train supervised learning models.\n- Optimize and evaluate models by using loss functions and performance metrics.\n- Create repeatable and scalable train, eval, and test datasets.\n- Implement ML models by using TensorFlow or Keras.\n- Understand the benefits of using feature engineering.\n- Explain Vertex AI Model Monitoring and Vertex AI Pipelines.","essentials_plain":"- Some familiarity with basic machine learning concepts.\n- Basic proficiency with a scripting language, preferably Python.","audience_plain":"- Aspiring machine learning data analysts, data scientists and data engineers\n- 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.","outline_plain":"Module 1 - Introduction to AI and Machine Learning on Google Cloud\n\n\nObjectives:\n\n\n\n- Recognize the AI\/ML framework on Google Cloud.\n- Identify the major components of Google Cloud infrastructure.\n- Define the data and ML products on Google Cloud and how they support the datato-AI lifecycle.\n- Build an ML model with BigQueryML to bring data to AI.\n- Define different options to build an ML model on Google Cloud.\n- Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training.\n- Use the Natural Language API to analyze text.\n- Define the workflow of building an ML model.\n- Describe MLOps and workflow automation on Google Cloud.\n- Build an ML model from end-to-end by using AutoML on Vertex AI.\n- Define generative AI and large language models.\n- Use generative AI capabilities in AI development.\n- Recognize the AI solutions and the embedded generative AI features.\n\nActivities:\n\n\n\n- Hands-On Labs\n- Module Quizzes\n- Module Readings\nModule 2 - Launching into Machine Learning\n\n\nObjectives:\n\n\n\n- Describe how to improve data quality.\n- Perform exploratory data analysis.\n- Build and train supervised learning models.\n- Describe AutoML and how to build, train, and deploy an ML model without writing a single line of code.\n- Describe BigQuery ML and its benefits.\n- Optimize and evaluate models by using loss functions and performance metrics.\n- Mitigate common problems that arise in machine learning.\n- Create repeatable and scalable training, evaluation, and test datasets.\n\nActivities:\n\n\n\n- Hands-On Labs\n- Module Quizzes\n- Module Readings\nModule 3 - TensorFlow on Google Cloud\n\n\nObjectives:\n\n\n\n- Create TensorFlow and Keras machine learning models.\n- Describe the TensorFlow main components.\n- Use the tf.data library to manipulate data and large datasets.\n- Build a ML model that uses tf.keras preprocessing layers.\n- Use the Keras Sequential and Functional APIs for simple and advanced model creation.\n- Train, deploy, and productionalize ML models at scale with the Vertex AI Training Service.\n\nActivities:\n\n\n\n- Hands-On Labs\n- Module Quizzes\n- Module Readings\nModule 4 - Feature Engineering\n\n\nObjectives:\n\n\n\n- Describe Vertex AI Feature Store.\n- Compare the key required aspects of a good feature.\n- Use tf.keras.preprocessing utilities for working with image data, text data, and sequence data.\n- Perform feature engineering by using BigQuery ML, Keras, and TensorFlow.\n\nActivities:\n\n\n\n- Hands-On Labs\n- Module Quizzes\n- Module Readings\nModule 5 - Machine Learning in the Enterprise\n\n\nObjectives:Understand the tools required for data management and governance.\n\n\n\n- Describe the best approach for data preprocessing: From providing an overview of Dataflow and Dataprep to using SQL for preprocessing tasks.\n- Explain how AutoML, BigQuery ML, and custom training differ and when to use a particular framework.\n- Describe hyperparameter tuning by using Vertex AI Vizier to improve model performance.\n- Explain prediction and model monitoring and how Vertex AI can be used to manage ML models.\n- Describe the benefits of Vertex AI Pipelines.\n- Describe best practices for model deployment and serving, model monitoring, Vertex AI Pipelines, and artifact organization.\n\nActivities:\n\n\n\n- Hands-On Labs\n- Module Quizzes\n- Module Readings","summary_plain":"This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques and feature engineering.","skill_level":"Intermediate","version":"3.5","duration":{"unit":"d","value":5,"formatted":"5 days"},"pricelist":{"List Price":{"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":3250},"US":{"country":"US","currency":"USD","taxrate":null,"price":2995},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":3190},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":3250},"IL":{"country":"IL","currency":"ILS","taxrate":17,"price":11270},"BE":{"country":"BE","currency":"EUR","taxrate":21,"price":2995},"NL":{"country":"NL","currency":"EUR","taxrate":21,"price":2995},"GR":{"country":"GR","currency":"EUR","taxrate":null,"price":3385},"MK":{"country":"MK","currency":"EUR","taxrate":null,"price":3385},"HU":{"country":"HU","currency":"EUR","taxrate":20,"price":3385},"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":3250},"SG":{"country":"SG","currency":"SGD","taxrate":8,"price":4140},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":3250},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":3300},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":4135},"FR":{"country":"FR","currency":"EUR","taxrate":19.6,"price":3770}}},"lastchanged":"2025-09-30T15:09:58+02:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/en\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course-schedule\/23770","source_lang":"en","source":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course\/google-mlgc"}}