The AWS Certified Machine Learning Specialty exam is a perfect way to start if you want to establish a career or expand your understanding of machine learning on the AWS Cloud. AWS, on the other hand, has consistently given the finest solutions for a variety of industries, supporting them in getting the best outcomes. Because of the addition of future technologies like Machine Learning, AWS’ services have gotten even more powerful. Many top organizations and enterprises profit from these areas since they reduce costs and resources.
As a result, completing the AWS Machine Learning Specialty exam will qualify you for AWS Machine Learning Specialty certification. You may use this to learn and develop ML abilities as you begin your career. The first stage, however, is to get certified and obtain skills, followed by getting a job to start your career. To put it another way, in this blog, we’ll go through all of the key topics and methodologies that can help you succeed as an AWS Machine Learning Developer.
Pathway for AWS Machine Learning Developer
Machine learning (ML) is a fast-developing technology that has the potential to produce millions of jobs and alter our way of life. The goal at AWS, on the other hand, is to put machine learning in the hands of every developer and data scientist. If you want to learn about machine learning in a creative manner, develop your professional skill set through online courses, or learn from other AWS engineers, you’ve come to the right place.
However, to get the most out of it, you’ll need to concentrate and work hard on a few key areas. The first requirement is that you pass the AWS Machine Learning speciality exam. So, let’s start by gaining a basic understanding of the exam and its requirements.
Step 1: Understanding the AWS Machine Learning Specialty Exam
If you are someone with expertise in artificial intelligence/machine learning (AI/ML) development or data science then, you should take the AWS Certified Machine Learning – Specialty (MLS-C01) exam. However, the exam verifies your competence to use the AWS Cloud to develop, construct, deploy, optimize, train, tune, and manage machine learning solutions for specific business challenges. Further, this exam will validate your skills and abilities for executing tasks such as:
- Choosing and justifying the best machine learning technique for a particular business challenge.
- Secondly, implementing ML solutions by identifying and using relevant AWS services.
- Lastly, creating scalable, cost-effective, dependable, and secure machine learning systems.
Focusing on the Knowledge Area
The ideal candidate for the AWS Certified Machine Learning – Specialty (MLS-C01) exam will have at least two years of experience designing, architecting, and deploying machine learning or deep learning workloads on the AWS Cloud. They should also be familiar with the following:
- The ability to communicate the intuition behind fundamental machine learning algorithms.
- Secndly, basic hyperparameter tuning experience is required.
- Experience with machine learning and deep learning frameworks is required.
- Lastly, the capacity to follow:
- best practises in model training.
- deployment best practises.
- best practises in operations.
Step 2: Exploring the Exam Format and working on study plan
The AWS Machine Learning Specialty Exam will have 65 multiple-choice and multiple-response questions. There is a time limit of 180 minutes to finish this exam. The exam is available in English, Japanese, Korean, and Simplified Chinese, with a registration fee of $300 USD. Further, you can take the exam through Pearson VUE or PSI, either at a testing facility or through an online proctored exam.
Talking about the study plan, understanding where to focus your efforts throughout the test preparation is one of the most important components of preparing for the AWS certification exam. Know the certification exam’s goals and evaluate your abilities, knowledge areas, concepts, and technologies. On the basis of this, create a study schedule to help you prepare for the exam and make sure you cover all of the exam objectives. As a result, we’ll go through the major training techniques and exam subjects to help you prepare better.
Step 3: Getting Familiar with the Exam Domains
It is recommended that you examine each topic presented for the AWS examination. The topics, on the other hand, are divided into parts and sub-sections. Applying the fundamentals of the topics can assist you in better preparing for the exam. The AWS Machine Learning Specialty Exam topics are:
Domain 1. Learn about Data Engineering
- Creating data repositories for machine learning.
- Identifying and applying a data ingestion solution.
- Identifying and applying a data transformation solution.
Domain 2. Understanding Exploratory Data Analysis
- Sanitizing and preparing data for modeling.
- Executing feature engineering.
- Analyzing and visualizing data for machine learning.
Use the AWS Machine Learning Specialty Tutorial for Complete Course Outline!
Domain 3. Overview of Modeling
- Framing business problems as machine learning problems.
- Selecting suitable model(s) for a given machine learning problem.
- Training machine learning models.
- Performing hyperparameter optimization.
- Evaluating machine learning models.
Domain 4. Learn about Machine Learning Implementation and Operations
- Developing machine learning systems with high performance, scalability, robustness, and fault tolerance.
- Recommending and applying suitable machine learning services and features for a given problem.
- Implementing basic AWS security practices to machine learning solutions.
- Deploying and operationalizing machine learning solutions.
Step 5: Using the AWS Training Methods
➼ Exam Readiness: AWS Certified Machine Learning – Specialty
The AWS Certified Machine Learning – Specialty exam verifies your knowledge of how to design, build, deploy, and manage machine learning (ML) systems. This course, on the other hand, will teach you about the exam’s logistics and mechanics, as well as the exam’s technical topics. Fundamental AWS services and important topics will be reviewed for the exam domains such as:
- Firstly, Data Engineering
- Secondly, Exploratory Data Analysis
- Thirdly, Modeling
- Lastly, Machine Learning Implementation and Operations
Course objectives
After completing the course, you will obtain skills in:
- Firstly, determining weaknesses in each exam topic for creating a focus and concentration on your preparation efforts.
- Secondly, describing the exam technical subjects and concepts.
- Thirdly, summarizing the exam’s logistics, questions and mechanics.
- Lastly, studying and taking the exam using effective strategies.
➼ Practical Data Science with Amazon SageMaker
You’ll learn how to use Amazon SageMaker to solve a real-world use case with machine learning (ML) and offer actionable findings in this intermediate-level course. This course walks you through the steps of a typical data science process for machine learning, from analyzing and visualizing a dataset through data preparation and feature engineering. Moreover, you’ll also learn how to construct models, train them, tweak them, and deploy them using Amazon SageMaker. Further, this training will also assist you in comprehending the following processes:
- Firstly, preparing a dataset for training
- Secondly, training and evaluating a machine learning model
- Thirdly, automatically tuning a machine learning model
- Lastly, creating a machine learning model for production
➼ The Machine Learning Pipeline on AWS
This course examines how to leverage the machine learning (ML) pipeline to solve a real-world business problem in a project-based learning setting. Students will learn about each component of the pipeline through instructor discussions and demonstrations. You’ll also be putting your abilities to work creating a solution that solves one of three business problems: fraud detection, recommendation engines, or airline delays. By the end of the course, you will have designed, trained, assessed, adjusted, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
However, in this course, you will understand the process of:
- Firstly, selecting a suitable ML approach for a given business problem
- Secondly, solving a specific business problem using the ML pipeline
- Then, training, evaluating, using, and tuning an ML model in Amazon SageMaker
- Lastly, explaining the best practices for creating scalable, cost-optimized, and secure ML pipelines in AWS
➼ CRISP-DM on the AWS Stack: Process Model
In the CRISP-DM paradigm, data science is viewed as a cyclical process. We’ll go through the CRISP-DM methodology and framework with Jake Chen, an AWS Data Science consultant, as well as how to apply the model’s six stages to your day-to-day work as a data scientist.
➼ The Elements of Data Science
Harsha Viswanath, a data scientist, will teach you how to build and update machine learning models, covering problem formulation, exploratory data analysis, feature engineering, model training, tuning, and debugging, as well as model assessment and production.
➼ Deep Learning on AWS
This one-day training will show you how to construct cloud-based deep learning applications using the AWS platform. You’ll learn how to run your models in the cloud using Amazon EC2’s deep learning Amazon Machine Image (AMI) and Apache MXNet on AWS frameworks. However, you’ll learn how to use Amazon SageMaker and deploy your deep learning models utilizing AWS services like AWS Lambda and Amazon Elastic Container Service while building intelligent systems on AWS (Amazon ECS). In addition, you will learn how to:
- Firstly, defining machine learning and deep learning
- Secondly, discovering the concepts in a deep learning ecosystem
- Thirdly, deep learning the workloads using Amazon SageMaker and MXNet programming frameworks
- Lastly, using appropriate AWS solutions for deep learning deployments
➼ AWS Hands-on–Learning
You can get started with machine learning right now with hands-on instructional gadgets. These devices make it simple to understand the principles of cutting-edge machine learning techniques including reinforcement learning, generative AI, and deep learning.
- AWS DeepRacer
- Then, AWS DeepComposer
- Lastly, AWS DeepLens
Step 6: Understand the AWS Machine Learning Plan
AWS Learning Plans offer a suggested set of digital courses to get you started. With the AWS Machine Learning Learning Plan, you won’t have to wonder if you’re starting in the appropriate place or taking the right courses. You will, however, be guided through an AWS-approved curriculum that you may finish at your own pace. Complete the whole plan or pick and choose the classes that interest you. You’ll be better equipped to create apps using AWS AI services and uncover real-world use cases for employing machine learning to address problems after completing this plan. You’ll have developed skills that will allow you to work as a machine learning developer, data scientist, or data engineer.
Step 7: Get yourself enrolled in Online Course
You’ll need a solid understanding of how to utilize the AWS Cloud to build, construct, deploy, optimize, train, tune, and manage machine learning solutions for specific business issues in order to pass the AWS Machine Learning Specialty Exam. Enroll in the AWS Machine Learning Training online course to learn more. It will also assist you in studying for the AWS exam. Expert assistance will also be available to assist you with any challenges or questions you may have. Here are a few online course providers who can help you become well-versed and equipped with in-depth knowledge so that you can pass the test.
- Udemy
- Coursera
- Testprep Training
- Simplilearn
Step 8: Using AWS Exam Practice tests
This is a crucial component of the study guide that will help you not only identify your weak areas but also construct a solid revision strategy. On the other hand, taking practice exams will help you enhance your answer abilities while also saving time. However, there are several free sample tests available to help you get started with AWS Machine Learning Specialty practice exams. You can utilize mock examinations as part of your revision once you’ve gone over a section or a few subjects.
Step 9: Start gaining hands-on experience
This is a necessary step in securing a well-paying and fulfilling job in the market. To put it another way, finding suitable employment will not be difficult if you have all of the necessary skills, experience, and have passed the AWS Machine Learning Specialist exam. As a result of this, you can begin working on field-related projects. Furthermore, after passing the AWS Machine Learning Specialist exam and using the skills and information you acquired, you may begin working on your own projects. This may be used as an assignment to assess your talents, as well as a method to show the firm your abilities during the interview.
Step 10: Applying for the job
After obtaining the AWS certification and getting hands-on experience, the next step is to pursue a top job in the industry. It’s also worth noting that being an AWS Machine Learning Specialist is the most efficient method to improve your machine learning engineer career. When it comes to the interview process, though, the first and most important thing to remember is to remain confident throughout. Secondly, you must prepare by reviewing both the theoretical and practical components of the project on which you collaborated. If you need more help, you may use the top AWS Machine Learning Specialist interview questions as a guide for your revision. This will help you cover all topics, starting with the fundamentals and advancing to more complex issues.
Things you must know
The average annual compensation for an Amazon Machine Learning Engineer is ₹650,656. Salary ranges from ₹134,969 to ₹19,38,642 per year for Machine Learning Engineers at Amazon. Additionally, the following are some of the top companies hiring for this role:
- Wipro
- Tech Mahindra
- AWS
- IBM
Final Words
We’ve covered the fundamentals of the AWS Machine Learning Specialist test and how to study for it. Furthermore, by studying what we can do to enhance our talents and knowledge in order to progress, we will have a better grasp of the post-exam process. This job, on the other hand, is all about your skills and experience. That is, you should pursue the AWS Machine Learning Specialty if you have expertise using the AWS Cloud to build, construct, deploy, optimize, train, tune, and manage machine learning solutions for specific business concerns. As a consequence, make sure your qualifications are met, prepare for the AWS exam, get experience, and start seeking work.
