课程: Google Cloud Professional Machine Learning Engineer Cert Prep

Course and Google Professional Machine Learning Engineer exam overview - Google Cloud Platform教程

课程: Google Cloud Professional Machine Learning Engineer Cert Prep

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Course and Google Professional Machine Learning Engineer exam overview

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- [Noah] Welcome to the Google Cloud Professional Machine Learning Engineer Certification. My name is Noah Gift. Let's talk a little bit about my background. I'm the founder of Pragmatic AI Labs. A training and consulting company that focuses on machine learning cloud and also DevOps. I'm an executive in residence at Duke. That means that I teach classes like machine learning, MLOps, cloud computing. I've also worked on the first 3D Animated Pipeline at Disney and other film studios like Sony Imageworks. And I also built a Sports Social Media Company with millions of users. And I consult with MLOps on Fortune 100 companies and also startups. And I've also been a bestselling author of five different O'Reilly books. And I've taught thousands of people Cloud Computing, Cloud Certification, and MLOps. All right, let's go ahead and talk about our agenda here. To start off with, we're going to dive into an overview of the exam. We'll also get into the overview of all the courses in this series. We'll also talk about a brief description of course one. Which is framing machine learning problems. Let's go ahead and dive into the Professional Machine Learning Engineer Exam. So in a nutshell, the exam's going to cover several key aspects of machine learning. You're going to need to know the details about how to take the exam. It's going to take about two hours. It's also going to encapsulate 50 to 60 questions. You can take the exam online. Or you can also take it in person. Another thing to remember is that it's recommended that you have three years of industry experience around data science and machine learning. And also that you have at least a year experience with the Google Cloud platform. And some of the key things to remember are that it is important to have real world experience. And you can try out things with the Google Cloud free cheer. And also you can use the exam guide to figure out more about the exam. And also do review questions. All right, let's talk about the six sections in the exam. To start with, there is the framing machine learning problems. Which is course one. Also, there is architecting machine learning solutions. Really building things at scale involving machine learning. In section three. Designing data preparation and processing systems. In Section four. Developing machine learning models. In section five. Automating and orchestrating machine learning pipelines. And in section six. Monitoring, optimizing and maintaining ML solutions. Let's dive into course one here. This is going to cover translating business challenges into ML use cases. Also, how to define a machine learning problem. Really, problem framing is a critical component of machine learning. Also, how to define business success criteria. And also how to identify a risk to the feasibility of a machine learning solution. For example, is going to require too much money to run your machine learning solution? These are critical things to consider when you're building a solution at scale. Alright, that's it for this introduction. We talked about framing machine learning problems in detail. Talked about some of the details of the exam. Let's go ahead and get started.

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