Preparing for Google Cloud Certification: Machine Learning Engineer Certificate Online

Enhance your machine learning skills with Google Cloud certificate courses that teach you about architecting, deploying and managing ML models including Vertex AI.

Franklin University has partnered with Coursera Campus to provide cutting-edge certificates to learners seeking to advance. Courses are open to all learners. No application required.

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Get unlimited access to over 7,000 offerings found on the Coursera website – including guided projects, specializations and professional certificates offered by hundreds of leading universities and companies. You also get access to all 39 professional certificates found in the Franklin Marketplace.

What You Will Learn

• Be introduced to the big data capabilities of Google Cloud and learn what skills are required to become a successful machine learning engineer
• Build, train and deploy machine learning models using cutting-edge Goole AI technology like TensorFlow
• Learn how Professional ML Engineers adopt Google Cloud for ML production projects
• Plan and prepare for the Google Cloud Professional Machine Learning Engineer certification exam

About the Google Cloud Professional Machine Learning Engineer certification

As emerging technologies advance so, too, can experienced and credentialed professional machine learning engineers. If you’re an ML engineer that designs, builds, tests and deploys ML models using Google Cloud technologies, and you want to advance in your career, then now is the time to prepare for Google Cloud Professional Machine Learning Engineer certification.

This certificate program is specially designed to help you systematically and sequentially prepare for industry-leading certification while you practice and hone your ML skills. 

Each of the nine courses that make up the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate program is self-paced so you can have the flexibility to study and prepare for certification on a schedule that works for you.

With this Professional Certificate, you’ll learn and practice applying machine learning principles and practices to train, retrain, deploy, schedule, monitor and improve ML models. This specialization also incorporates hands-on projects covering a variety of topics, such as Google Cloud Platform (GCP) products, which you’ll use and configure within Google Qwiklabs.

If you're looking to validate your cloud skills and have more confidence in your machine learning capabilities, this Professional Certificate program is for you.

Required Google Cloud Machine Learning Engineer Certificate Courses

Introduction to AI and Machine Learning on Google Cloud

BEGINNER | Information Technology | Self-paced | 10 hours

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.

Launching into Machine Learning

BEGINNER | Data Science | Self-paced | 14 hours

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

Build, Train and Deploy ML Models with Keras on Google Cloud

INTERMEDIATE | Data Science | Self-paced | 13 hours

This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

Feature Engineering

INTERMEDIATE | Data Science | Self-paced | 8 hours

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.

Machine Learning in the Enterprise

INTERMEDIATE | Data Science | Self-paced | 19 hours

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.

Production Machine Learning Systems

ADVANCED | Data Science | Self-paced | 19 hours

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.

Machine Learning Operations (MLOps): Getting Started

INTERMEDIATE | Data Science | Self-paced | 3 hours

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 <<<

ML Pipelines on Google Cloud

ADVANCED | Data Science | Self-paced | 5 hours

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle. Please take note that this is an advanced level course and to get the most out of this course, ideally you have the following prerequisites: You have a good ML background and have been creating/deploying ML pipelines You have completed the courses in the ML with Tensorflow on GCP specialization (or at least a few courses) You have completed the MLOps Fundamentals course. >>> 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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