GCP Professional Machine Learning Engineer Certification Guide

The Professional Machine Learning Engineer certification is crucial for aspiring ML engineers seeking to validate their skills and knowledge.

### Preparing for GCP Professional Machine Learning Engineer Certification
* 🎥 The video emphasizes the **importance of clear concepts** and invites viewers to engage in the comments with **questions and suggestions** while encouraging **likes and subscriptions**.
* 📊 The passing percentage for the GCP Professional Machine Learning Engineer exam is believed to be around **70%**, which is lower than the **80%** for data engineering exams due to the machine learning exam's **increased difficulty**.
* 📅 A recommended preparation strategy includes **booking the certification 1 to 2 months in advance** to create a sense of urgency and focus on studying.
* 🛠 Key topics include architecting **low-code ML solutions** using BigQuery ML and AutoML, and managing datasets and data models within **collaborative teams**.
* 🤖 The certification is aimed at ML engineers and aspiring professionals, covering **fundamental concepts to advanced topics** like **neural networks** and **operationalizing machine learning models**.
* 📚 The preparation process involves going through documentation, hands-on projects on GCP, and taking mock tests, with an emphasis on **understanding limitations and best practices**.
* ⏳ The exam consists of about 50 questions over 2 hours, so **time management is crucial**, and it's suggested to practice with platforms like **Exam Topic** for real exam scenarios.
* 💡 Ultimately, the certification adds value to a resume, providing **credibility from a recognized provider like Google**, especially for those with existing skills and experience in machine learning.

there are two types of people in this world. Some believe that certifications are great, and others believe that certifications are worthless. So, should a company hire a candidate with certification or without certification? Well, there's no simple answer to it. So, if you measure certificate versus skill, obviously, skills should be prioritized more than certifications. But if you do have skills and hands-on experience, and if you decide to do a relevant certification, then that adds a cherry on top in your resume. They kind of put a stamp on your resume from an external third-party company like Amazon, Google, Microsoft, for example. If you choose to do cloud-based certifications, and it has more credibility than doing some other types of certifications from Udi or whatnot. Now, I talked about this point in one of my earlier videos where I talked about all different types of certifications possible and which one should you aim for. But this video is different. In this video, I'm going to talk about GCP machine learning engineer certification. This is a certification that I recently did, and I told you guys

about it in my last video, and the reason I'm not in the office yet is that I had a professional machine learning engineer exam of GCP, and I received an enormous response to make a video on this so that they can decide if they want to aim for the certification, and if they want to go for it, how should they prepare for it. So, here we are. If you don't know me, I'm Josh, and I'm an AI engineer at Google, and in this video, we're not only going to talk about how to prepare for machine learning engineer certification, but we'll start all the way from, is this certification even worth it? Second, what different things you will learn as a part of this certification course? And lastly, how to clear this exam, like what resources to refer to, what kind of mock tests to give before you sit in the actual exam. But before we jump into that, do not forget to leave a like and subscribe to the channel. That helps us out a lot. After you have done that, let's get

started see. I have given multiple certifications. I have given three AWS certifications, one snowflake certification, and two GCP certifications. But this one PMLE, which is like Professional Machine Learning Engineer, was by far the toughest certification exam that I've had. But who is this for? Well, this is for ML Engineers or aspiring ML engineers who want to learn everything about ML, starting with how to develop a machine learning model, how to productionize it, how to operationalize it in the long term. It starts with at a very fundamental level, so it talks about like precision and recall definition, and then it goes all the way to neural network architectures, and then Transformer architectures, and whatnot. So, if you're a data scientist or ML engineer or even a developer with a passion for machine learning or AI, this certification is your golden ticket. It shows developers how you can pick real-world problems and solve them using machine learning. But one thing that I would like to highlight is this certification is not for complete beginners on GCP. So, for example, if you have had no hands-on experience

on GCP at all, then I don't recommend directly going and you know booking this exam and going for this certification. In that case, what you can do is just get a free tier GCP account. You'll also get around 300 credits or $300 worth of credits when you create it, so you can use that for like your PC purposes. And you can probably create a project on GCP. I'm going to link some interesting projects in the description below, so pick one of those, and then you'll have a good idea about what GCP is, how things work in it. If you already have hands-on experience on GCP or if you have done any previous GCP certification, then you don't need to do all that. You can directly jump right into this exam. Now that we have covered who this certification is for, let's talk about what different topics are covered. So for this, let's look at the official certification guide, and the good thing about this guide is it doesn't only have different topics, but it also has the breakdown of

percentage of different topics that would appear in the actual exam. Uh, this is the high-level summary, and then you can see the section one is architecting low-code ML solutions. So, this section is all about using BigQuery machine learning or AutoML. So, it's like AutoML is a product where you can do drag and drop and develop machine learning models. BigQuery ML is you can develop machine learning models using SQL. So, this is like low-code or no-code machine learning solution. So, this part talks about that. Uh, it also talks about APIs, like in which you don't have to develop any other models, you can directly use pre-built Google APIs for your applications. Then, the next section is collaborating within and across teams to manage data and data models. So, it's about how to organize different types of data, how to manage datasets, how to do pre-processing, which is pretty much the job of data engineers, creating and consolidating features, right? It also talks about how to do model prototyping in notebooks, and then finally

tracking and running machine learning experiments. The section three is scaling prototypes into machine learning models. As you can see, it's like building models, training models, and then choosing appropriate hardware for training. It also talks about how to use distributed hardware or distributed compute when you are training your machine learning models. Section four talks about serving and scaling models, batch or online, or more like real-time inference. It also talks about AB testing versions, scaling online model serving. So, once you have, like, let's say, served your machine learning models, what different products like feature store that you can use. And then the next question is automating or orchestrating ML pipelines. Now, obviously, you have done all the work manually first, but in going forward, when you want to deploy a new version of the model, you don't want the pipeline to be manual. For the first time, it's okay. So, this section talks about how to automate and orchestrate machine learning pipelines. It talks about important libraries like MLflow, Cloud Composer, Kubeflow, Vertex AI.

pipelines which can be used to orchestrate the machine learning workflow. Uh, it also talks about how to track and audit metadata throughout the pipeline, and then section six is about monitoring machine learning solutions. Now that you have done everything, you have automated it, right? But over time in the production, you have to monitor your machine learning solutions because if your model gives like 90% accuracy now, it's definitely going to deteriorate over time because the data that it's going to see after 5 years will be very different compared to when it was trained on. So maybe the accuracy drops to let's say 80 or 85%. You need to continuously monitor the data shift or data skew over time, and you have to retrain the model when required. So this section talks about that. I would just like to highlight that the exam overall has about 50 questions, and it lasts about 2 hours. So you have approximately 2 minutes per question, so you have to be really mindful of the time limit. So if you are taking more than like 1 or 1.5

minutes in a question. You should train your brain to think about, like, Let's flag this question and move on to the next one. And about the passing percentage, so there's no specific published passing percentage, but it is believed to be around 70% for this exam, which is a little lower compared to other GCP professional exams, like data engineer exams, which is 80%. But that is because this machine learning exam is significantly more difficult or challenging compared to the data engineering exam. So even like GCP knows that, and that's why they have reduced the passing threshold a little. So now that we know about the exam, and we also know about the topics that we will learn throughout the certification, let's go to the most important part of this video, which is the preparation strategy. So number one is book your certification in advance. So you want to book your certification 1 to 2 months in advance, depending on how confident you are, and then start preparing. What it will do is it will create a pressure for you so that you'll

have to learn almost every day, uh, because you know that your exam is coming up, and it will also, like, keep you motivated. Now, the second point is to divide your time into three sections. So, number one, the first phase should be course completion. I took this course for my machine learning preparation, and I recommend all of you guys to take the same. The course name itself is Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate. So, this is not a single course, but it's like an umbrella of eight different courses. So, it will take a lot of your time. So, let's look at what different courses are covered. So, it's like Introduction to AI and Machine Learning on Google Cloud, uh, Launching into Machine Learning, which double clicks on different ML concepts and products, TensorFlow on Google Cloud. So, as we know, TensorFlow and Keras are the most popular libraries in machine learning, even to this day. So, this, uh, course talks about that. And then, Feature Engineering. This section you'll find very similar to, like, data engineering things if you have experience with that. Next is Machine

learning in Enterprise, so which talks about data management governance. It also talks about hyperparameter tuning and how to create batch and online predictions and then production machine Learning System. So, it's about how to scale your models into production and then finally MLOps. As I said, over time, you need to operationalize the pipeline, you need to automate things, and you also need to monitor it. So, this one talks about all that and then ML pipelines on Google Cloud. So, how you can orchestrate your workflow using different products like Cloud Composer or Data Flow or Vertex AI pipelines on Google Cloud. So, that's the first phase. It can last anywhere between 20 to 40 days, depending on the skill set of a person and also depending on how much hands-on they are. Following next is going through documentation. So, going through documentation is absolutely key for these kinds of certification exams because the course will teach you about the products, it will teach you about the fundamentals and everything, but it will not teach you about all the limitations or all the best practices of a given

product. So, for example, Vortex AI is a very widely used product in all the ML/AI things that you do on Google Cloud. So, this entire documentation of Vex AI is super useful, and it has so many beginner guides as well, and it has different tutorials that you can follow through as well. Start with this Get Started section and then go through this entire documentation. It talks about how to use machine learning models for image, for tabular data, for text data, and it also has different kinds of labs within that if you want to follow that with hands-on. But if you have done hands-on in the course itself, you don't need to do that in the documentation. This phase might take about 7 to 15 days, depending on your speed. And then, the last phase of any AI certification exam, probably the most important phase, is going through the mock test. You should spend about a week to 10 days in giving different mock test exams. I can give you a very good website to start with. You can go to Exam Topic.

So, as you can see, there are 285 questions here. You can just click on these questions. A lot of them are from previous exams that have been actually asked in the certification exam before. You can see that the right solution here is C, but the A solution is the most voted. So, this is a very common problem with exam topics or even any other such websites, is that they get answers often wrong. But the good thing about exam topics is it lets people vote on the right solution, and then people discuss it in the discussion section. So, if I click on discussion, you will be able to see how many people voted for A and then what was their reasoning behind that. I would say that most of the time, the most voted solution is the right one. But one thing that you'll run into is that it will only give you about 20 or so pages, and then it will ask you for a paid subscription. I don't feel that the paid subscription of exam topics is worth doing. So, what I did instead is there is a good Udemy course.

that has about 350 or so exam questions, and it also has properly organized exams. So, there are like five or six different exams, and each exam has 50 different questions. They are time-bounded, just like the real exam. So, if you start that, you will get the real exam feeling. And it also has, in the end, you can see the actual solution. The author has described the right answers, but the thing is, even this, like 90% of the questions are right, but not all of them. But the good thing is, a lot of people have commented on it, and then the author has responded that yes, your reasoning is correct. And then he has gone back and changed the right answer in his mock tests. I also did a couple of comments on some of the questions, and he agreed to my point of view and changed the answers. A lot of people like me have already done that exercise of providing feedback and giving the actual right answer. So, if you take this course, there is a very good amount of chance that most of the answers that you'll find are correct.

one you cover about 300 to 350 questions before the exam, chances are 60 to 70% of the questions will be very similar to those questions. Throughout all of these three different phases, the important thing is to make notes, either make handwritten notes or maybe create a document in your laptop that you can follow through. So, for example, these are my notes. So, I created different sections for course one, for exam one, for documents, and then one for extras. So, every weekend, I used to go through these notes, and it would obviously incrementally take more amount of time for you to go through your notes because they get bigger. This is very useful that you do revision in exams. So, for example, this is the question that I had a problem with when I was solving the mock test, and then this is the answer, and this is the reasoning behind that, and then some additional documentation, and then again, the next question that I faced difficulty with, its answer, and then its justification. So, doing this will help you a lot, will make sure that

your concepts are crystal clear, and that's really important. Yes, that's it. Uh, I hope you guys enjoyed this video, and if you have any questions, any suggestions, comments, thoughts, let's talk about it in the comment section below. And as I said, do not forget to leave the like and subscribe to the channel. Uh, that's it from my side. See you next time. [Music]

ML Engineer Cert Review + The ONLY Study Plan You'll Need

Jash Radia

The GCP Machine Learning Engineer Certification

There are differing opinions on the value of certifications, but skills and hands-on experience should be prioritized.

The GCP machine learning engineer certification offers a good opportunity, especially with a free tier account and $300 in credits.

The certification covers topics such as running machine learning experiments, scaling prototypes, serving models, and automating ML pipelines.

Preparing for the GCP Machine Learning Engineer Certification

Josh, an AI engineer at Google, shares insights on preparing for the GCP Machine Learning Engineer certification.

The video covers the certification's worth, the skills learned, and resources for exam preparation, including mock tests.

Josh highlights the common issue of wrong answers on exam preparation websites and suggests a Udemy course as an alternative to the paid subscription for additional resources.

Professional Machine Learning Engineer Certification

The Professional Machine Learning Engineer certification is designed for ML engineers and aspiring professionals, covering the entire machine learning workflow from model development to long-term operationalization.

It emphasizes real-world problem-solving using machine learning and is not suitable for complete beginners on GCP.

The exam consists of 50 questions over 2 hours, and preparation involves hands-on experience, studying Vortex AI documentation, and taking mock tests.

Comprehensive Exam Preparation and Machine Learning Course

The course offers around 350 exam questions organized into five or six time-bounded exams, providing a real exam experience.

The author actively responds to feedback and updates the mock test answers, ensuring a high accuracy rate.

The course covers data management, hyperparameter tuning, production machine learning systems, MLOps, and ML pipelines on Google Cloud, with the first phase lasting 20 to 40 days.

Effective Exam Preparation for Machine Learning

Making notes is crucial for exam preparation, with a structured approach and regular revision.

Dividing time into three phases, starting with course completion, is recommended, with the umbrella course for machine learning preparation covering eight different courses.

Key topics include AI and machine learning introduction, ML concepts and products, TensorFlow on Google Cloud, and feature engineering.

Preparing for the GCP Machine Learning Exam

The GCP Machine Learning exam has a passing percentage believed to be around 70%, lower than other professional exams due to its difficulty.

Booking the certification 1 to 2 months in advance is recommended to create pressure for preparation.

The exam covers topics like architecting low-code ML solutions using BigQuery and AutoML, managing data and data models, and model prototyping in notebooks.