Preparing for Google Cloud Certification: Machine Learning Engineer

Google Cloud via Coursera Professional Certificate

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Overview

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87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification. Here's what you have to do 1) Complete the Preparing for Google Cloud Machine Learning Engineer Professional Certificate 2) Review other recommended resources for the Google Cloud Professional Machine Learning Engineer exam 3) Review the Professional Machine Learning Engineer exam guide 4) Complete Professional Machine Learning Engineer sample questions 5) Register for the Google Cloud certification exam (remotely or at a test center) Applied Learning Project This professional certificate incorporates hands-on labs using Qwiklabs platform. These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.

Syllabus

Course 1: Introduction to AI and Machine Learning on Google Cloud
Course 2: Build, Train and Deploy ML Models with Keras on Google Cloud
Course 3: Feature Engineering
Course 4: Machine Learning in the Enterprise
Course 5: Production Machine Learning Systems
Course 6: Machine Learning Operations (MLOps): Getting Started

Courses

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17 hours 47 minutes
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This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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8 hours 25 minutes
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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13 hours 18 minutes
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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18 hours 46 minutes
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In this course, we dive into the components and best practices of building high-performing ML systems in production environments. We cover some of the most common considerations behind building these systems, e.g. static training, dynamic training, static inference, dynamic inference, distributed TensorFlow, and TPUs. This course is devoted to exploring the characteristics that make for a good ML system beyond its ability to make good predictions.
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3 hours 32 minutes
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models. This course is primarily intended for the following participants: Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact. Software Engineers looking to develop Machine Learning Engineering skills. ML Engineers who want to adopt Google Cloud for their ML production projects. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
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8 hours 54 minutes
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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 based on the different goals of users, including data scientists, AI developers, and ML engineers.