{"id":78386,"date":"2021-04-14T20:09:14","date_gmt":"2021-04-15T01:39:14","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=78386"},"modified":"2023-12-29T01:44:04","modified_gmt":"2023-12-29T07:14:04","slug":"google-professional-machine-learning-engineer-certification-complete-guide","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/","title":{"rendered":"Google Cloud Certified Professional Machine Learning Engineer Certification &#8211; Complete Guide"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">In this topic, we are going to guide you through the complete process of <a href=\"https:\/\/www.whizlabs.com\/professional-machine-learning-engineer\/\" target=\"_blank\" rel=\"noopener\"><strong>Google Cloud Certified Professional Machine Learning Engineer Certification<\/strong> <\/a>from where you can opt for the courses, what topics are going to be covered in this certification, and how you can prepare for this certification.<\/span><\/p>\n<p><em>Let&#8217;s dive in:<\/em><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ea7e02;color:#ea7e02\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ea7e02;color:#ea7e02\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Google_Cloud_Certified_Professional_Machine_Learning_Engineer_Certification\" >Google Cloud Certified Professional Machine Learning Engineer Certification<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Career_for_AI_and_ML_in_Google\" >Career for AI and ML in Google<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#PortionsSyllabus_for_the_Exam\" >Portions\/Syllabus for the Exam<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Section_1_ML_Problem_Framing\" >Section 1: ML Problem Framing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Section_2_ML_Solution_Architecture\" >Section 2: ML Solution Architecture<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Section_3_Data_Preparation_and_Processing\" >Section 3: Data Preparation and Processing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Section_4_ML_Model_Development\" >Section 4: ML Model Development<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Section_5_ML_Pipeline_Automation_Orchestration\" >Section 5: ML Pipeline Automation &amp; Orchestration<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Section_6_ML_Solution_Monitoring_Optimization_and_Maintenance\" >Section 6: ML Solution Monitoring, Optimization, and Maintenance<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Learning_Path_Google_Cloud_Certified_Professional_Machine_Learning_Engineer_Certification\" >Learning Path : Google Cloud Certified Professional Machine Learning Engineer Certification<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Courses_for_Google_Cloud_Certified_Professional_Machine_Learning_Engineer_Certification\" >Courses for Google Cloud Certified Professional Machine Learning Engineer Certification<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Preparing_for_Google_Cloud_Professional_Machine_Learning_Engineer_Certification\" >Preparing for Google Cloud Professional Machine Learning Engineer Certification<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#Details_Regarding_the_Exam\" >Details Regarding the Exam<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.whizlabs.com\/blog\/google-professional-machine-learning-engineer-certification-complete-guide\/#FAQs\" >FAQs<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Google_Cloud_Certified_Professional_Machine_Learning_Engineer_Certification\"><\/span><b>Google Cloud Certified Professional Machine Learning Engineer Certification<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Professional machine learning engineer is this certification provided by Google Cloud in the field of machine learning. According to the Google Cloud, a professional machine learning engineer always helps to design, build, and productionize the machine learning models to solve various business challenges using Google Cloud air technologies and all the knowledge of the machine learning models.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Google Cloud Professional Machine Learning Engineer exam help you to assess the following skills:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Frame machine learning problems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Architecture machine learning solutions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Prepare and process data<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Developing machine learning models<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Automate and orchestrate machine learning pipelines\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Monitoring, optimizing, and maintaining machine learning solutions.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">To give this exam Google recommends you need to have at least 3 years of experience in the field of machine learning so you can have a proper understanding while preparing for the exam.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Career_for_AI_and_ML_in_Google\"><\/span><b>Career for AI and ML in Google<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Most of the people working in Google in Machine Learning and AI aren\u2019t truly developers but most of them are Research Scientists. This means they hold a PhD degree and have a considerable amount of research experience. Many branches of Google such as Google DeepMind have a minimum education qualification requirement of PhD.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Talking about developers, Google\u2019s software engineers\/developers\/programmers work in various departments and fields. So, if you want to join just as a developer then its way is the same as any other programmer position.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It\u2019s no surprise that the\u00a0Artificial intelligence\u00a0talent market is white-hot at present. In fact, Gartner maintains that the business value of AI will stand at $3.9 trillion in 2022., while\u00a0IDC\u00a0estimates that the worldwide spending on cognitive and artificial intelligence systems will reach $77.6 billion by 2022.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Career paths for machine learning engineers are<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Machine learning engineer<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data scientist<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">NLP scientist<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI\/ML developer<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">And many more<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Organization working with google AI and ML technology are as follows.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Bright star<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Geotab<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Blazeclan<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Therap and more<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"PortionsSyllabus_for_the_Exam\"><\/span><b>Portions\/Syllabus for the Exam<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Section_1_ML_Problem_Framing\"><\/span><b>Section 1: ML Problem Framing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h6><span style=\"font-weight: 400;\">1.1 Translate business challenge into ML use case. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Defining business problems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identifying nonML solutions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Defining output use<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Managing incorrect results<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identifying data sources<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">1.2 Define ML problem. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Defining problem type (classification, regression, clustering, etc.)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Defining outcome of model predictions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Defining the input (features) and predicted output format<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">1.3 Define business success criteria. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Success metrics<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Key results<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Determination of when a model is deemed unsuccessful<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">1.4 Identify risks to feasibility and implementation of ML solution. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Assessing and communicating business impact<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Assessing ML solution readiness<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Assessing data readiness<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Aligning with Google AI principles and practices (e.g. different biases)<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Section_2_ML_Solution_Architecture\"><\/span><b>Section 2: ML Solution Architecture<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h6><span style=\"font-weight: 400;\">2.1 Design reliable, scalable, highly available ML solutions. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Optimizing data use and storage<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data connections<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Automation of data preparation and model training\/deployment<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SDLC best practices<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">2.2 Choose appropriate Google Cloud software components. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A variety of component types &#8211; data collection; data management<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Exploration\/analysis<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Feature engineering<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Logging\/management<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Automation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Monitoring<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Serving<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">2.3 Choose appropriate Google Cloud hardware components. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Selection of quotas and compute\/accelerators with components<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">2.4 Design architecture that complies with regulatory and security concerns. <\/span><span style=\"font-weight: 400;\">Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Building secure ML systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Privacy implications of data usage<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identifying potential regulatory issues<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Section_3_Data_Preparation_and_Processing\"><\/span><b>Section 3: Data Preparation and Processing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h6><span style=\"font-weight: 400;\">3.1 Data ingestion. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop\/Spark)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Database migration<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Streaming data (e.g. from IoT devices)<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">3.2 Data exploration (EDA). Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Visualization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Statistical fundamentals at scale<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Evaluation of data quality and feasibility<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">3.3 Design data pipelines. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Batching and streaming data pipelines at scale<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data privacy and compliance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Monitoring\/changing deployed pipelines<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">3.4 Build data pipelines. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data validation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Handling missing data<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Handling outliers<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Managing large samples (TFRecords)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Transformations (TensorFlow Transform)<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">3.5 Feature engineering. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data leakage and augmentation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Encoding structured data types<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Feature selection<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Class imbalance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Feature crosses<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Section_4_ML_Model_Development\"><\/span><b>Section 4: ML Model Development<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h6><span style=\"font-weight: 400;\">4.1 Build a model. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Choice of framework and model<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Modeling techniques given interpretability requirements<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Transfer learning<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model generalization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Overfitting<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">4.2 Train a model. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Productionizing<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Training a model as a job in different environments<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tracking metrics during training<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Retraining\/redeployment evaluation<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">4.3 Test a model. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Unit tests for model training and serving<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model performance against baselines, simpler models, and across the time dimension<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model explainability on Cloud AI Platform<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">4.4 Scale model training and serving. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Distributed training<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hardware accelerators<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Section_5_ML_Pipeline_Automation_Orchestration\"><\/span><b>Section 5: ML Pipeline Automation &amp; Orchestration<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h6><span style=\"font-weight: 400;\">5.1 Design pipeline. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identification of components, parameters, triggers, and compute needs<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Orchestration framework<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hybrid or multi-cloud strategies<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">5.2 Implement training pipeline. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Decoupling components with Cloud Build<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Constructing and testing of parameterized pipeline definition in SDK<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Tuning compute performance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Performing data validation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Storing data and generated artifacts<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">5.3 Implement serving pipeline. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model binary options<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Google Cloud serving options<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Testing for target performance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Setup of trigger and pipeline schedule<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">5.4 Track and audit metadata. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Organization and tracking experiments and pipeline runs<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hooking into model and dataset versioning<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model\/dataset lineage<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">5.5 Use CI\/CD to test and deploy models. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hooking models into existing CI\/CD deployment system<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A\/B and canary testing<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Section_6_ML_Solution_Monitoring_Optimization_and_Maintenance\"><\/span><b>Section 6: ML Solution Monitoring, Optimization, and Maintenance<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<h6><span style=\"font-weight: 400;\">6.1 Monitor ML solutions. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Performance and business quality of ML model predictions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Logging strategies<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Establishing continuous evaluation metrics<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">6.2 Troubleshoot ML solutions. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Permission issues (IAM)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Common training and serving errors (TensorFlow)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ML system failure and biases<\/span><\/li>\n<\/ul>\n<h6><span style=\"font-weight: 400;\">6.3 Tune performance of ML solutions for training &amp; serving in production. Considerations include:<\/span><\/h6>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Optimization and simplification of input pipeline for training<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Simplification techniques<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Identification of appropriate retraining policy<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Learning_Path_Google_Cloud_Certified_Professional_Machine_Learning_Engineer_Certification\"><\/span><b>Learning Path : <\/b><b>Google Cloud Certified Professional Machine Learning Engineer Certification<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Google itself has a learning path defined for machine learning courses in an order so that you can understand machine learning easily and can prepare for the Google Cloud Professional machine learning engineer certification simultaneously. Based on the portions are the syllabus present for the certification you can opt for the courses all training that is available in machine learning path buy Google so that you can prepare for the certification exam.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Courses_for_Google_Cloud_Certified_Professional_Machine_Learning_Engineer_Certification\"><\/span><b>Courses for <\/b><b>Google Cloud Certified Professional Machine Learning Engineer Certification<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">There are various places where you can check for the cost of Google Cloud Professional machine learning engineer certification. Most of them cover all the portions that are required for this certification and provide hands-on experience for this. Google itself provides a crash course available for machine learning which will help you to understand the basics of machine learning with the available service in the Google Cloud the URL for the crash course is pasted below. there are other places where you can check for certification courses like Udemy and YouTube for free courses.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Machine Learning Crash course by Google: &#8211; <\/span><a href=\"https:\/\/developers.google.com\/machine-learning\/crash-course\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">https:\/\/developers.google.com\/machine-learning\/crash-course<\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Preparing_for_Google_Cloud_Professional_Machine_Learning_Engineer_Certification\"><\/span><b>Preparing for <\/b><b>Google Cloud Professional Machine Learning Engineer Certification<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">There are different ways to prepare for this exam. Google suggested some of the steps to be performed in order before the exam so that you can have complete preparation for the certification.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The steps suggested by Google are as follows:\u00a0<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Get real-world experience: To have real-world experience on machine learning projects so that you can have a better understanding of machine learning technology and terminologies.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Understand what is on the exam: what topics will be on the exam so that you can study easily we have already covered this part in the previous section.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Review the sample questions: Google already has a place where they have posted a sample question that comes according to an exam you can have a look at that sample questions to prepare yourself and solve some of the model questions or mock exams.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Round out your skills with training: It&#8217;s better to practice all the services that are provided by Google Cloud for machine learning to have a better hands-on experience and understanding.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Schedule an exam: After you have completed all the above steps you can now schedule an exam according to your availability and your readiness\u00a0<\/span><\/li>\n<\/ol>\n<h3><span class=\"ez-toc-section\" id=\"Details_Regarding_the_Exam\"><\/span><b>Details Regarding the Exam<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><span style=\"font-weight: 400;\">Format: &#8211; A total of 60 questions and each question are multiple choice.\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Examination method: &#8211; onsite center or online exam.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Time: &#8211; 120 minutes<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Cost: &#8211; 200 U.S. dollars\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Language available: &#8211; English\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Certification Validity: &#8211; 2 years<\/span><\/li>\n<li><b>Schedule an exam for <\/b><b>Google Cloud Certified Professional Machine Learning Engineer Certification: <\/b><a href=\"https:\/\/cloud.google.com\/certification\/machine-learning-engineer\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">https:\/\/cloud.google.com\/certification\/machine-learning-engineer<\/span><\/a><b>\u00a0<\/b><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"FAQs\"><\/span><b>FAQs<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><b>Q1<\/b><span style=\"font-weight: 400;\">. Does Whizlabs have a course for Google Cloud Certified Professional Machine Learning Engineer Certification?<\/span><\/p>\n<p><b>Ans<\/b><span style=\"font-weight: 400;\">. Yes, Whizlabs has the course for Google Cloud Certified Professional Machine Learning Engineer Certification.\u00a0<\/span><\/p>\n<p><b>Q2<\/b><span style=\"font-weight: 400;\">. Where can I find the Google Learning path?<\/span><\/p>\n<p><b>Ans<\/b><span style=\"font-weight: 400;\">. Here is the link you can find more about Google Learning Path<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span><a href=\"https:\/\/cloud.google.com\/training\/machinelearning-ai\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">https:\/\/cloud.google.com\/training\/machinelearning-ai<\/span><\/a><\/p>\n<p><b>Q3<\/b><span style=\"font-weight: 400;\">. Do google have any criteria for the exam?<\/span><\/p>\n<p><b>Ans<\/b><span style=\"font-weight: 400;\">. Google does say that to examinee must be at least of age 18.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><b>Q4<\/b><span style=\"font-weight: 400;\">. When can we give the exam for certification renewal?<\/span><\/p>\n<p><b>Ans<\/b><span style=\"font-weight: 400;\">. You need to start 60 prior for the expiration.<\/span><\/p>\n<p><b>Q5<\/b><span style=\"font-weight: 400;\">. When can we retake the exam if we fail?<\/span><\/p>\n<p><b>Ans<\/b><span style=\"font-weight: 400;\">. If you fail the Exam, you may retake the Exam, but you must wait at least fourteen (14) days before doing so. If you fail the Exam a second time, you may retake the Exam, but must wait at least sixty (60) days before doing so. If you fail the Exam a third time, you may retake the Exam, but you must wait at least one (1) year before doing so.<\/span><\/p>\n<p><b>Q6<\/b><span style=\"font-weight: 400;\">. Is Google Leaning Path is free?<\/span><\/p>\n<p><b>Ans<\/b><span style=\"font-weight: 400;\">. Google learning path provides you 30 days free trail.<\/span><\/p>\n<p><b>Q7<\/b><span style=\"font-weight: 400;\">. Do we receive any vouchers after clearing the exam?<\/span><\/p>\n<p><b>Ans<\/b><span style=\"font-weight: 400;\">. No we do not receive any vouchers after clearing an exam.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this topic, we are going to guide you through the complete process of Google Cloud Certified Professional Machine Learning Engineer Certification from where you can opt for the courses, what topics are going to be covered in this certification, and how you can prepare for this certification. Let&#8217;s dive in: Google Cloud Certified Professional Machine Learning Engineer Certification Professional machine learning engineer is this certification provided by Google Cloud in the field of machine learning. According to the Google Cloud, a professional machine learning engineer always helps to design, build, and productionize the machine learning models to solve various [&hellip;]<\/p>\n","protected":false},"author":13,"featured_media":78387,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[12],"tags":[1804,1805,4114],"class_list":["post-78386","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-google-cloud","tag-google-machine-learning","tag-google-machine-learning-course","tag-google-professional-machine-learning-engineer"],"uagb_featured_image_src":{"full":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15.png",2240,1260,false],"thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15-150x150.png",150,150,true],"medium":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15-300x169.png",300,169,true],"medium_large":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15-768x432.png",768,432,true],"large":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15-1024x576.png",1024,576,true],"1536x1536":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15-1536x864.png",1536,864,true],"2048x2048":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15-2048x1152.png",2048,1152,true],"profile_24":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15.png",24,14,false],"profile_48":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15.png",48,27,false],"profile_96":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15.png",96,54,false],"profile_150":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15.png",150,84,false],"profile_300":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15.png",300,169,false],"tptn_thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15-250x250.png",250,250,true],"web-stories-poster-portrait":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15.png",640,360,false],"web-stories-publisher-logo":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15.png",96,54,false],"web-stories-thumbnail":["https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/Blog-15.png",150,84,false]},"uagb_author_info":{"display_name":"Pavan Gumaste","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/pavan\/"},"uagb_comment_info":221,"uagb_excerpt":"In this topic, we are going to guide you through the complete process of Google Cloud Certified Professional Machine Learning Engineer Certification from where you can opt for the courses, what topics are going to be covered in this certification, and how you can prepare for this certification. Let&#8217;s dive in: Google Cloud Certified Professional&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/78386","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=78386"}],"version-history":[{"count":7,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/78386\/revisions"}],"predecessor-version":[{"id":91355,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/78386\/revisions\/91355"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/78387"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=78386"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=78386"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=78386"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}