Shadowing Practice: AWS Machine Learning Engineer Associate: What You Need to Know - Learn English Speaking with Video
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The AWS Certified Machine Learning Engineer Associate is one of the hottest and newest certifications in all of tech.
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If you're considering this certification or curious about how you prepare, in this video I'm going to tell you exactly what you need to know.
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If you're new here, I'm Greg, creator of Thoughtful Techie Cloud.
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And each week I'm bringing you a simple to understand video to help you navigate your AWS cloud and tech journey.
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If you haven't already, what are you waiting for?
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Go ahead and subscribe right now.
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built up enough confidence to actually schedule it.
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This video, my aim is to give you the system
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that you can put together so you can come up with
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that to make your decision whether or not this is a certification for you.
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And I'm going to highlight tons of resources and techniques that you can use to prepare for this.
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And I promise you, this video will not disappoint.
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Now, I'm going to go over a lot of information here, but I want you to sit back,
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relax, and just chill because you know I'm going to put those links for you in the description below.
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I got you.
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I always like to start these videos by going over what I call the AWS certification hierarchy.
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If you're new to AWS, there's going to be four buckets of certification.
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There's going to be the foundational bucket.
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These are going to be knowledge-based certifications for foundational understanding of AWS cloud.
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No prior experience is needed.
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So this is kind of like just starting out.
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Then we're going to go to the associate level.
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These are role-based certifications that showcase your knowledge and skills on AWS and help you build your credibility on AWS cloud.
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As you notice here, the machine learning engineer associate fits into the associate level.
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As you progress throughout your studies, you can bump up to the professional level.
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Right now, solution architect professional as well as DevOps engineer professionals are a choice.
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These are also role-based certifications, but they're going to validate your advanced skills and knowledge required to design secure,
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optimize, and modernize applications and automate processes on AWS.
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And then specialty, there are three right now.
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This is going to allow you to dive deeper and position yourself as a trusted advisor in specific areas.
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But our focus today is going to be on the Machine Learning Engineer Associate.
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We're in an interesting phase for this certification right now.
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It is currently in beta.
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The AWS Certified Machine Learning Engineer Associate is going to validate your technical ability in implementing machine learning workloads
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and production and operationalizing them.
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I really want to focus your attention on that implementing those ML workloads and operationalizing them.
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This certification is not just about knowing what something is or knowing when to use a service in this particular use case.
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You need to have implementation level knowledge.
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This is definitely going to boost your career profile and credibility if you manage to clear this exam.
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And you're going to position yourself for in-demand machine learning job roles, which are all the rage right now.
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Super in-demand.
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The exam duration while this thing is in beta is 170 minutes there are 85 questions
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and the current cost is 75 USD.
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Intended candidates are individuals with at least one year of experience using Amazon SageMaker and other ML engineering AWS services.
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And the candidate role examples of somebody that may be interested in taking this would be back-end software developers,
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DevOps engineers, data engineers, ML ops engineers, and data scientists.
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Now, if none of that rings a bell or aligns directly with you.
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Don't worry.
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You don't need to throw your hands up and say, this is not for me.
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You can still power through.
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You just may need to be able to take some additional training to be able to supplement where you have any gaps.
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So not to worry there.
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One of the greatest things you can do as a first
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step in preparation for this certification is to review the exam guide.
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This full exam guide for this certification is 23 pages.
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Now, don't worry.
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I'm not going to read everything word for word, but I am going to highlight your attention to things
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that you should pay particular close attention to and get you familiar with the overall structure of this exam guide.
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I highly encourage you after you watch this full video to go and first thing you do,
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download this exam guide and read it from start to finish because it's going to help you out immensely.
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Again, I want to reiterate here that this exam will validate your ability to build,
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operationalize, deploy, and maintain machine learning solutions and pipelines using AWS Cloud.
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Now, I really want to be specific here with these tasks.
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You're going to need to be familiar with things like ingest, transform, validate, and prepare data for ML modeling.
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Because guess what?
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You can't just take raw data and train your models against it.
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You really need to go through that phase to clean that data to get it ready for ML modeling.
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You're going to need to be able to select general modeling approaches, how to train models, tune hyper parameters, analyze model performance, and manage model versions.
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What about deployment and choosing endpoints and infrastructure to support those endpoints?
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You're going to need to have familiarity with that.
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Things such as provisioning the necessary compute resources and configuring auto-scaling policies based on requirements for your workload.
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Continuous integration and continuous delivery pipelines to automate and orchestrate your ML workflows are also gonna be needed for this exam.
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As I mentioned in the overview, monitoring models, data, and infrastructure to detect any issues in your ML ops pipelines also necessary.
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And one thing we can't forget, security of your ML systems and resources through access controls, compliance features, and security best practices.
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Now let's take a look at some recommended general IT knowledge.
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The target candidate should have the following general IT knowledge.
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A basic understanding of common ML algorithms and their use cases is going to be beneficial to you.
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Data engineering fundamentals, including knowledge of common data formats, the ingestion and transformation to work with ML data pipelines.
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I mentioned that earlier, that whole ingest and transform that whole bit there.
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That's the data engineering fundamentals and knowledge of querying and transforming data.
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Additionally, knowledge of software engineering best practices for modular reusable code development,
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deployment, debugging, familiarity with provisioning and monitoring cloud on-premises, ML resources, the CI-CD pipelines I mentioned earlier,
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and experience with code repositories for version control for those CI-CD pipelines.
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Recommended AWS knowledge, again, there's gonna be some overlap here with what we've talked about, but this is from an AWS perspective.
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Knowledge of SageMaker capabilities and algorithms for building and deployment.
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Knowledge of AWS data storage and processing services.
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Again, that's gonna support preparing the data for the modeling.
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Familiarity with deploying applications and infrastructure on AWS.
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Monitoring tools, logging and troubleshooting ML systems.
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Knowledge of AWS services specifically to help you automate and orchestrate a CI CD pipeline.
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And I mentioned security, but from an AWS perspective, things such as identity and access management,
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encryption and data protection within AWS gonna help you out big time.
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Now, just as important as it is to understand what tasks are necessary for you to know to prepare for the exam,
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these are actually some job tasks that are out of scope for the target candidate.
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It's not an exhaustive list, but just a few things you can cross off the list of things you need to worry about.
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You don't need to worry about designing and architecting full end-to-end ML solutions.
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You don't need to worry about setting up best practices and guiding ML strategies.
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You don't need to worry about handling integration with a wide array of services or new tools and technologies.
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You don't need to know how to work deeply into a more ML domains, for example, natural language processing and computer vision.
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Thank goodness there.
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And you don't need to worry about quantizing models and analyzing the impact on accuracy.
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Thank goodness to all of those.
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Although I think you'll still have your hands full with some of the information that we mentioned earlier.
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Super important exam content as it relates to question types.
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Now, AWS certifications have always had the multiple choice and multiple response,
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but with this certification, AWS is introducing some additional question types that you might not be familiar with, so listen closely.
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So multiple choice, these have been around for a while, it's gonna have one correct response and three incorrect responses known as distractors.
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Multiple response is gonna have two or more correct responses out of five, and you must select all the correct responses to receive credit for the question.
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Let's talk about these new question types, ordering.
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So ordering questions are gonna have a list of three to five responses to complete a specific task,
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and you must select the correct responses and literally place the responses in the correct order to receive credit for the question.
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Matching.
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Now, this is going to be questions where you have a list of responses to match.
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It can be three to seven prompts, and you must match all those pairs correctly to receive credit for the question.
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Case studies, final question type here, is you're going to have a scenario, and it's going to have two or more questions associated with the scenario.
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The scenario is the same for each question in the case study, and each question in the case study will be evaluated separately.
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you'll receive credit for each question that you answer correctly in that case study.
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Something else you do not want to gloss over are the domains.
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There's going to be four domains on this test.
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And the domains are simply the areas that you need to focus on in preparation for the exam.
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And the weightings, which means the scored content, how much of this topic you can find out of 100% on the exam.
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So for example, domain one is data preparation for machine learning.
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it's going to be 28% of scored content.
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Domain two, ML model development, 26% of scored content is going to be based on domain two.
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Domain three, deployment and orchestration of ML workflows, going to be 22% of scored content.
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And then domain four, ML solution monitoring, maintenance, and security worth 24% of scored content.
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Now, as an example, I want to go over domain one
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because this exam guide really drills down into each of these domains.
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Under those domain categories are what are known as task statements.
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There are a handful of task statements for domain
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that give you the knowledge that you need to be familiar with as well as skills in the map to this.
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I'm not going to go over these word for word
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but I just want to show you how this is formatted
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and the fact that you do need to download this
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and read it in its entirety
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because this is going to be a goldmine of insight to help you direct your energies in your preparation.
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So task statement 1.1 aligned with domain 1 is focusing on the ingesting and storing data.
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And we talked about this earlier in some of those tasks.
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Things like be familiar with data formats and ingestion mechanisms,
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how to use core AWS services like Amazon S3 and Amazon Elastic File System and Amazon FSX,
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how to use AWS streaming data sources to ingest data, AWS storage options including use cases and trade-offs in making those choices.
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In the skills department, extracting data from storage,
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choosing appropriate data formats, ingesting data in Amazon SageMaker Data Wrangler and SageMaker Feature Store.
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A ton of insight in each one of these task statements as it aligns to the domain.
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I recommend you read this document in its entirety.
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And you follow this format for all the domains.
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For example, domain two, which is focused on ML model development.
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Task statement 2.1 is choosing a modeling approach.
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It has the knowledge as well as the skills that align to that task statement for that domain.
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Make sure you check these out for the entire exam guide.
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And real quick, before we move away from this exam guide, there's an appendix right it has in scope aws services
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and features that you should know about
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so they break these out into different categories you have analytics
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category application integration cloud financial management compute containers database developer tools
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and within each one of these categories you'll have a list
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of aws services additionally you have out of scope aws services and features
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and these will be considered out of scope for this exam this is about working smart not working hard now
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if you're still here got a couple of surprises for you
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got some free training for you i want to point your
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attention to the standard exam prep aws certified machine learning engineer associate training this is around a seven
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and a half hours worth of free training it's going to
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include introduction to machine learning art of the possible planning a machine learning project
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and an amazon bedrock getting started training as well as the exam prep standard course itself.
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That course is going to go over getting to know the exam and the exam style questions.
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It's going to talk about refreshing your AWS knowledge and skills, review and practice, and then it's going to go into domain one,
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two, three, and four in detail to really help you prepare for this certification.
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And if you're still here, I've got another surprise for you.
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AWS Power Hour for ML Engineer Associate.
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This is a training that AWS Training and Certification puts on.
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You can watch these AWS Power Hours live if it aligns with your schedule.
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If not, they are recorded so you can watch them on demand as well.
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There are six episodes starting with the episode one exam introduction and then every episode beyond that,
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episode two, three, four, and five is aligned with the domains of the certification itself.
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And then finally, episode six will include review and practice.
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We covered a ton of stuff.
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All the links to everything we talked about are in the description below.
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Before you go, check out this video right here, and I'll see you in the next video.
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