GCPMLEGOGoogleGO-GCPMLE1.0<ul> <li>List the domains covered on the Professional Machine Learning Engineer (PMLE) certification exam.</li><li>Identify gaps in your knowledge and skills for each domain.</li><li>Identify resources and learning assets available to develop your knowledge and skills.</li><li>Create a study plan to prepare for the PMLE certification exam.</li></ul><p>Googlers, partners, and customers</p><ul> <li>Introduction</li><li>Architecting low-code AI solutions</li><li>Collaborating within and across teams to manage data and models</li><li>Scaling prototypes into ML models</li><li>Serving ML models</li><li>Automating and orchestrating ML pipelines</li><li>Monitoring ML Solutions</li><li>Your next steps</li></ul><h5>Module 01 Architecting low-code AI solutions</h5><h6>Topics</h6><ul> <li>Ira needs to understand customer segments using BigQuery and a clustering model.</li><li>Sasha needs to predict customer value using AutoML Cymbal Retail&rsquo;s customer dataset.</li><li>Taylor needs to build a conversational AI assistant for customers using Vertex AI Agent Builder and retrieval-augmented generation (RAG)</li><li>Diagnostic questions</li><li>Review and study planning</li></ul><h6>Objectives</h6><ul> <li>Identify your level of knowledge in developing and implementing BigQuery ML and AutoML machine learning solutions.</li><li>Determine the skills needed to select appropriate ML APIs, prepare data effectively, and build custom models using AutoML.</li></ul><h6>Activities</h6><ul> <li>Lecture</li><li>Diagnostic questions</li><li>Quiz</li></ul><h5>Module 02 Collaborating within and across teams to manage data and models</h5><h6>Topics</h6><ul> <li>Use Google Cloud&#039;s products and Cymbal Retail&#039;s rich data to design a model to predict which high-value customers are likely to stop purchasing (also known as customer churn).</li><li>Answer diagnostic questions.</li><li>Review the information and plan your study.</li></ul><h6>Objectives</h6><ul> <li>Identify your level of knowledge in exploring, preprocessing, and managing organization-wide data.</li><li>Identify your level of knowledge in addressing privacy implications and leveraging tools like Vertex AI Feature Store.</li><li>Determine the skills needed to prototype models using Jupyter notebooks on Google Cloud.</li><li>Determine the skills needed to select appropriate backends, implement security best practices, and integrate with code repositories.</li></ul><h6>Activities</h6><ul> <li>Lecture</li><li>Diagnostic questions</li><li>Quiz</li></ul><h5>Module 03 Scaling prototypes into ML models</h5><h6>Topics</h6><ul> <li>Use Google Cloud&#039;s products and Cymbal Retail&#039;s rich data to build and scale customer churn prototype into a production-ready model</li><li>Answer diagnostic questions.</li><li>Review the information and plan your study.</li></ul><h6>Objectives</h6><ul> <li>Identify your level of knowledge in scaling ML prototypes into production-ready models</li><li>Identify your level of knowledge in selecting appropriate ML frameworks, model architectures, and modeling techniques based on interpretability requirements.</li><li>Determine the skills needed to train models effectively, including organizing and ingesting training data on Google Cloud.</li><li>Determine the skill needed to utilize distributed training techniques, perform hyperparameter tuning, and troubleshoot training failures.</li></ul><h6>Activities</h6><ul> <li>Lecture</li><li>Diagnostic questions</li><li>Quiz</li></ul><h5>Module 04 Serving ML models</h5><h6>Topics</h6><ul> <li>Use Google Cloud&#039;s products and Cymbal Retail&#039;s rich data to deploy a customer churn model and use it in production for inference.</li><li>Answer diagnostic questions.</li><li>Review the information and plan your study.</li></ul><h6>Objectives</h6><ul> <li>Identify the level of knowledge needed to effectively serve models in production.</li><li>Identify the level of knowledge needed to select between batch and online inference, utilize various serving frameworks, organize a model registry, and conduct A/B testing for model optimization.</li><li>Determine the skills needed to scale online model serving, including leveraging Vertex AI Feature Store.</li><li>Determine the skills needed to manage public and private endpoints, choose appropriate hardware, optimize serving backends for throughput, and fine-tune models for optimal performance in production.</li></ul><h6>Activities</h6><ul> <li>Lecture</li><li>Diagnostic questions</li><li>Quiz</li></ul><h5>Module 05 Automating and orchestrating ML pipelines</h5><h6>Topics</h6><ul> <li>Use Google Cloud&rsquo;s products to orchestrate the entire machine learning pipeline for seamless execution and continuous improvement with customer churn.</li><li>Answer diagnostic questions.</li><li>Review the information and plan your study.</li></ul><h6>Objectives</h6><ul> <li>Identify the level of knowledge needed to develop and maintain end-to-end ML pipelines.</li><li>Identify the level of knowledge needed to validate data and model, consistent preprocessing, hosting options, component identification, parameterization, triggering mechanisms, compute needs, orchestration strategies.</li><li>Determine the skills needed to automate model retraining, including establishing retraining policies.</li><li>Determine the skills needed to implement CI/CD model deployment, and track and audit metadata (model artifacts, versions, data lineage).</li></ul><h6>Activities</h6><ul> <li>Lecture</li><li>Diagnostic questions</li><li>Quiz</li></ul><h5>Module 06 Monitoring ML Solutions</h5><h6>Topics</h6><ul> <li>Use Google Cloud&rsquo;s products to ensure the customer churn model remains robust, reliable, and aligned with Google&rsquo;s Responsible AI principles.</li><li>Answer diagnostic questions.</li><li>Review the information and plan your study.</li></ul><h6>Objectives</h6><ul> <li>Identify the level of knowledge needed to assess and mitigate risks in ML solutions.</li><li>Identify the level of knowledge needed to build secure ML systems, align with responsible AI practices, evaluate solution readiness, and utilize model explainability on Vertex AI.</li><li>Determine the skills needed to monitor, test, and troubleshoot ML solutions.</li><li>Determine the skills needed to establish continuous evaluation metrics, monitor for training-serving skew and feature drift, compare model performance against baselines, and investigate common training and serving errors.</li></ul><h6>Activities</h6><ul> <li>Lecture</li><li>Diagnostic questions</li><li>Quiz</li></ul><h5>Module 07 Your next steps</h5><h6>Topics</h6><ul> <li>A sample study plan for the exam</li><li>How to register for the exam</li></ul><h6>Objectives</h6><ul> <li>Review a sample study plan for the exam</li><li>Learn how to register for the exam</li></ul><h6>Activities</h6><ul> <li>Create your study plan for the exam</li><li>Identify a date to take the exam based upon your plan</li><li>Register for the exam</li></ul>- List the domains covered on the Professional Machine Learning Engineer (PMLE) certification exam. - Identify gaps in your knowledge and skills for each domain. - Identify resources and learning assets available to develop your knowledge and skills. - Create a study plan to prepare for the PMLE certification exam.Googlers, partners, and customers- Introduction - Architecting low-code AI solutions - Collaborating within and across teams to manage data and models - Scaling prototypes into ML models - Serving ML models - Automating and orchestrating ML pipelines - Monitoring ML Solutions - Your next stepsModule 01 Architecting low-code AI solutions Topics - Ira needs to understand customer segments using BigQuery and a clustering model. - Sasha needs to predict customer value using AutoML Cymbal Retail’s customer dataset. - Taylor needs to build a conversational AI assistant for customers using Vertex AI Agent Builder and retrieval-augmented generation (RAG) - Diagnostic questions - Review and study planning Objectives - Identify your level of knowledge in developing and implementing BigQuery ML and AutoML machine learning solutions. - Determine the skills needed to select appropriate ML APIs, prepare data effectively, and build custom models using AutoML. Activities - Lecture - Diagnostic questions - Quiz Module 02 Collaborating within and across teams to manage data and models Topics - Use Google Cloud's products and Cymbal Retail's rich data to design a model to predict which high-value customers are likely to stop purchasing (also known as customer churn). - Answer diagnostic questions. - Review the information and plan your study. Objectives - Identify your level of knowledge in exploring, preprocessing, and managing organization-wide data. - Identify your level of knowledge in addressing privacy implications and leveraging tools like Vertex AI Feature Store. - Determine the skills needed to prototype models using Jupyter notebooks on Google Cloud. - Determine the skills needed to select appropriate backends, implement security best practices, and integrate with code repositories. Activities - Lecture - Diagnostic questions - Quiz Module 03 Scaling prototypes into ML models Topics - Use Google Cloud's products and Cymbal Retail's rich data to build and scale customer churn prototype into a production-ready model - Answer diagnostic questions. - Review the information and plan your study. Objectives - Identify your level of knowledge in scaling ML prototypes into production-ready models - Identify your level of knowledge in selecting appropriate ML frameworks, model architectures, and modeling techniques based on interpretability requirements. - Determine the skills needed to train models effectively, including organizing and ingesting training data on Google Cloud. - Determine the skill needed to utilize distributed training techniques, perform hyperparameter tuning, and troubleshoot training failures. Activities - Lecture - Diagnostic questions - Quiz Module 04 Serving ML models Topics - Use Google Cloud's products and Cymbal Retail's rich data to deploy a customer churn model and use it in production for inference. - Answer diagnostic questions. - Review the information and plan your study. Objectives - Identify the level of knowledge needed to effectively serve models in production. - Identify the level of knowledge needed to select between batch and online inference, utilize various serving frameworks, organize a model registry, and conduct A/B testing for model optimization. - Determine the skills needed to scale online model serving, including leveraging Vertex AI Feature Store. - Determine the skills needed to manage public and private endpoints, choose appropriate hardware, optimize serving backends for throughput, and fine-tune models for optimal performance in production. Activities - Lecture - Diagnostic questions - Quiz Module 05 Automating and orchestrating ML pipelines Topics - Use Google Cloud’s products to orchestrate the entire machine learning pipeline for seamless execution and continuous improvement with customer churn. - Answer diagnostic questions. - Review the information and plan your study. Objectives - Identify the level of knowledge needed to develop and maintain end-to-end ML pipelines. - Identify the level of knowledge needed to validate data and model, consistent preprocessing, hosting options, component identification, parameterization, triggering mechanisms, compute needs, orchestration strategies. - Determine the skills needed to automate model retraining, including establishing retraining policies. - Determine the skills needed to implement CI/CD model deployment, and track and audit metadata (model artifacts, versions, data lineage). Activities - Lecture - Diagnostic questions - Quiz Module 06 Monitoring ML Solutions Topics - Use Google Cloud’s products to ensure the customer churn model remains robust, reliable, and aligned with Google’s Responsible AI principles. - Answer diagnostic questions. - Review the information and plan your study. Objectives - Identify the level of knowledge needed to assess and mitigate risks in ML solutions. - Identify the level of knowledge needed to build secure ML systems, align with responsible AI practices, evaluate solution readiness, and utilize model explainability on Vertex AI. - Determine the skills needed to monitor, test, and troubleshoot ML solutions. - Determine the skills needed to establish continuous evaluation metrics, monitor for training-serving skew and feature drift, compare model performance against baselines, and investigate common training and serving errors. Activities - Lecture - Diagnostic questions - Quiz Module 07 Your next steps Topics - A sample study plan for the exam - How to register for the exam Objectives - Review a sample study plan for the exam - Learn how to register for the exam Activities - Create your study plan for the exam - Identify a date to take the exam based upon your plan - Register for the exam1 day650.00650.00650.00650.00790.00650.00650.00