AWS Machine Learning Specialist is a professional who is responsible for designing, building, deploying, and maintaining machine learning (ML) solutions in the Amazon Web Services (AWS) cloud. They work with customers to identify business problems that can be solved with machine learning, and then design and implement solutions to solve those problems. There is a growing demand for AWS Machine Learning Specialists as more and more businesses are adopting machine learning to improve their operations and customer experience. According to the latest Report, machine learning engineers, which includes AWS Machine Learning Specialists, were among the top emerging jobs. The report also highlights that machine learning engineers have a high annual median salary and a 74% annual growth rate in hiring. This growing demand is expected to continue, making AWS Machine Learning a lucrative career path.
This blog aims to provide aspiring professionals with an overview of how to build a career as an AWS Machine Learning Specialist. It will cover the technical and soft skills required for the role, education and certification requirements, and ways to gain relevant experience and advance in the field.
Overview of Machine Learning in AWS
Machine learning (ML) is an exciting and rapidly evolving technology with the potential to create millions of jobs and change how we live our lives. However, the objective at AWS is to get machine learning into the hands of every developer and data scientist. You’ve come to the correct spot whether you want to study ML in a fun way, improve your professional skill set with online courses, or learn from other AWS engineers.
And, in order to get the best out of this, you need to focus and work hard on some major things. The first one is to pass the AWS Machine learning speciality exam. So, let’s begin with the roadmap to achieve your dream job role.
Getting prepared for AWS Machine Learning Specialty Exam
This AWS Certified Machine Learning – Specialty (MLS-C01) exam helps in the identification and development of candidates with skills for implementing the cloud. The ability to design, train, tune, and deploy machine learning (ML) models on AWS will be validated by getting this certification. However, if you work in artificial intelligence/machine learning (AI/ML) development or data science then you should take the test. 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, the exam also validates the skills for executing various tasks like:
- Firstly, selecting and justifying a suitable ML approach for a given business problem
- Secondly, identifying suitable AWS services for implementing ML solutions
- Lastly, planning and applying scalable, cost-optimized, reliable, and secure ML solutions
Knowledge requirement
For this exam:
- Firstly, you must have experience of 2 or more years for developing, architecting, and running ML or deep learning workloads in the AWS Cloud.
- Secondly, you must have skills for expressing the intuition behind basic ML algorithms
- Thirdly, it is necessary to have experience in:
- performing basic hyperparameter optimization
- ML and deep learning frameworks
- Lastly, you must have the abiltuty for following:
- model-training best practices
- deployment best practices
- operational best practices
How to become a AWS Machine Learning Specialist?
AWS is one of the most well-known and profitable cloud computing companies on the market, and it needs to gain the company’s confidence. As a result, obtaining your AWS certification early in your career will be advantageous. However, to give clarity, we’ll go over some of the steps you should take to get started as an AWS Machine Learning Specialist.
1. Skills to build a career as an AWS Machine Learning Specialist:
Technical Skills:
- Knowledge of Programming Languages: As an AWS Machine Learning Specialist, proficiency in programming languages such as Python, Java, and R is essential. Knowledge of SQL and familiarity with data manipulation tools like Pandas, Numpy, and Scikit-Learn is also necessary.
- Familiarity with Machine Learning Frameworks: Familiarity with machine learning frameworks such as TensorFlow, Keras, and PyTorch is critical. The ability to develop and deploy machine learning models using AWS services such as SageMaker and Lambda is also required.
- Cloud Computing Experience: As an AWS Machine Learning Specialist, it is important to have experience working with AWS services such as EC2, S3, and RDS. Experience with serverless computing, containers, and other cloud-native technologies is also beneficial.
Soft Skills:
- Communication Skills: As an AWS Machine Learning Specialist, you will be working with a team of professionals, including business stakeholders, data scientists, and software developers. Excellent communication skills are necessary to convey technical information to non-technical stakeholders and to collaborate effectively with team members.
- Collaboration Skills: Working as part of a team is essential in developing effective machine learning solutions. You should have the ability to work collaboratively with data scientists, software developers, and business stakeholders to develop and deploy ML solutions.
- Problem-Solving Skills: As an AWS Machine Learning Specialist, you will be tasked with solving complex business problems using machine learning. Strong problem-solving skills are necessary to identify the root cause of a problem and develop effective solutions. The ability to identify potential roadblocks and to develop workarounds is also necessary.
2. Start gaining hands-on experience
This is an essential step in obtaining a well-paid and rewarding career in the market. To put it another way, if you have all of the necessary abilities, experience, and have passed the AWS Machine Learning Specialist exam, finding a suitable job will not be difficult. You can begin working on initiatives relating to the field as a result of this. Furthermore, you may start working on your own projects after passing the AWS Machine Learning Specialist test by applying the skills and knowledge you learned. This may be used as an assignment to measure your abilities, and it will also help you demonstrate your abilities to the firm during the interview.
- Internships: Internships can provide valuable experience working with machine learning frameworks and AWS services. Many organizations offer internships that provide hands-on experience with machine learning tools and technologies.
- Freelance Projects: Freelance projects can provide opportunities to work on real-world problems, build a portfolio of projects, and gain experience working with clients. Freelance platforms such as Upwork and Freelancer offer a range of opportunities to work on machine learning projects.
- Working in a Machine Learning Team: Working in a machine learning team can provide exposure to working with experienced professionals and provide opportunities to learn from their experience. Joining a team can help you build a network of professionals and gain valuable experience working with machine learning frameworks and AWS services.
3. Focus on Networking Skills
- Joining Professional Associations: Joining professional associations such as the Association for Computing Machinery (ACM) or the Institute of Electrical and Electronics Engineers (IEEE) can provide opportunities to network with other professionals in the field. These organizations often offer events, conferences, and webinars that can help you stay up-to-date on the latest trends and technologies in the field.
- Attending Conferences and Meetups: Attending conferences and meetups can provide opportunities to network with other professionals in the field and learn about the latest technologies and trends. AWS re:Invent, Machine Learning Conference, and AI Summit are examples of conferences that provide opportunities to learn and network.
- Connecting with Professionals on LinkedIn: LinkedIn is a valuable platform for networking with other professionals in the field. You can join groups related to machine learning and AWS, connect with professionals, and participate in discussions. Building a network of professionals on LinkedIn can provide opportunities for collaboration, job opportunities, and mentorship.
4. Dealing with the job interview
The next stage is to seek a top position in the sector after acquiring the AWS certification and gaining hands-on experience. It’s also important to note that gaining an AWS Machine Learning Specialist certification is the most effective way to advance your career as a machine learning engineer. However, when it comes to the interview process, the first and most crucial thing to remember is to remain confident during the interview.
Secondly, you must prepare by going over both the theoretical and practical aspects of the project you worked on. You may also utilize the top AWS Machine Learning Specialist interview questions as a reference for your revision if you need additional support. This will assist you in covering all topics, beginning with the basics and progressing to more sophisticated questions.
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 best companies to look for while applying for the AWS Machine Learning Specialist job:
- Wipro
- Tech Mahindra
- AWS
- IBM
Exam Preparation Tips and Strategies
Step 1. Overview of Exam Objective
It is advised that you go through each topic provided for each AWS test and review it. On the other hand, the topics are organized into sections and sub-sections. Understanding the fundamentals of the topics can help you prepare for the exam more effectively. The following are the main points to consider:
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.
Use the AWS Machine Learning Specialty Tutorial for Complete Course Outline!
Domain 2. Understanding Exploratory Data Analysis
- Sanitizing and preparing data for modeling.
- Executing feature engineering.
- Analyzing and visualizing data for machine learning.
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 2. AWS Training Course
Exam Readiness: AWS Certified Machine Learning – Specialty
The AWS Certified Machine Learning – Specialty exam validates your ability to develop, create, deploy, and manage machine learning (ML) systems. However, you’ll learn about the exam’s logistics and the mechanics of exam questions, as well as the exam’s technical areas, in this course. For the exam domains, you’ll review fundamental AWS services and key concepts:
- Firstly, Data Engineering
- Secondly, Exploratory Data Analysis
- Thirdly, Modeling
- Lastly, Machine Learning Implementation and Operations
Course objectives
After completing the course, you will gain skills to:
- Firstly, determine your skills and weaknesses in each exam topic so you know where to concentrate your preparation efforts.
- Secondly, describe the technical subjects and concepts that each exam domain is made up of.
- Thirdly, summarize the exam’s logistics and mechanics, as well as the questions.
- Lastly, study and take the exam using effective strategies.
Practical Data Science with Amazon SageMaker
In this intermediate-level course, you’ll learn how to utilize Amazon SageMaker to solve a real-world use case with machine learning (ML) and provide actionable results. From evaluating and visualizing a dataset through data preparation and feature engineering, this course leads you through the phases of a typical data science workflow for machine learning. Moreover, you will also learn how to use Amazon SageMaker to design models, train them, tune them, and deploy them. Further, this course will help in understanding the process of:
- Firstly, preparing a dataset for training
- Secondly, training and examining a machine learning model
- Thirdly, automatically tuning a machine learning model
- Lastly, preparing a machine learning model for production
The Machine Learning Pipeline on AWS
In a project-based learning environment, this course examines how to use the machine learning (ML) pipeline to address a real-world business problem. Instructor talks and demonstrations will teach students about each component of the pipeline. Moreover, you will be using your skills for finishing a product that addresses one of three business issues: fraud detection, recommendation engines, or airline delays. Furthermore, you will have developed, trained, assessed, tweaked, and deployed an ML model using Amazon SageMaker that answers their chosen business challenge by the conclusion of the course.
However, in this course, you will learn the process of:
- Firstly, selecting and justifying a suitable ML approach for a given business problem
- Secondly, using the ML pipeline for solving a specific business problem
- Then, training, examining, deploying, and tuning an ML model in Amazon SageMaker
- Lastly, explaining the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
CRISP-DM on the AWS Stack: Process Model
Data science is framed as a cyclical undertaking in the CRISP-DM paradigm. With Jake Chen, an AWS Data Science consultant, we’ll go through the CRISP-DM methodology and framework, as well as how to apply the model’s six stages to your day-to-day job as a data scientist.
The Elements of Data Science
In this, Data Scientist Harsha Viswanath will teach you how to construct and upgrade machine learning models, including issue formulation, exploratory data analysis, feature engineering, model training, tuning, and debugging, as well as model assessment and productionizing.
Deep Learning on AWS
This one-day training will teach you how to use the AWS platform to create cloud-based deep learning applications. You’ll learn how to use Amazon EC2’s deep learning Amazon Machine Image (AMI) and Apache MXNet on AWS frameworks to run your models in the cloud. However, while creating intelligent systems on AWS, you’ll also learn how to utilize Amazon SageMaker and deploy your deep learning models using AWS services like AWS Lambda and Amazon Elastic Container Service (Amazon ECS). Further, you will learn the process of:
- Firstly, defining machine learning and deep learning
- Secondly, identifying the concepts in a deep learning ecosystem
- Thirdly, utilizing Amazon SageMaker and MXNet programming frameworks for deep learning workloads
- Lastly, fitting AWS solutions for deep learning deployments
AWS Hands-on–Learning
With hands-on instructional gadgets, you can get started with machine learning immediately. These gadgets make studying the fundamentals of cutting-edge machine learning techniques like reinforcement learning, generative AI, and deep learning simple.
- AWS DeepRacer
- Then, AWS DeepComposer
- Lastly, AWS DeepLens
AWS Machine Learning Learning Plan
AWS Learning Plans provide a recommended collection of digital courses to help beginners get started. You won’t have to question if you’re starting in the proper location or taking the right courses with AWS Machine Learning Learning Plan. However, you’ll be taken through an AWS-recommended curriculum that you may complete at your own speed. Complete the whole plan or select the classes that appeal to you. After completing this plan, you’ll be better prepared to design apps utilizing AWS AI services and discover real-world use cases for leveraging machine learning to solve issues. You’ll have acquired abilities that will enable you to pursue professions as a machine learning developer, data scientist, or data engineer. And, you’ll also gain a deeper grasp of Amazon Web Offerings (AWS) services such as Amazon SageMaker and others.
AWS Ramp-Up Guide
This downloadable guide was created by AWS professionals to help you explore the vast array of tools and information available to help you grow your machine learning skills—all in one location. You may utilize this guide at your own speed, whether you choose to read articles, examine PDFs, or take digital courses. It will assist you in comprehending all of your learning alternatives and determining which are the most appropriate for you depending on your knowledge and ability level.
Step 3. 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.
However, we learned how to pass the exam in the previous sections. But what comes next?
Final Words
If you have a passion for data and a desire to make a difference, pursuing a career in AWS machine learning could be a great choice. Here are some reasons why:
- High Demand: There is a growing demand for machine learning specialists with experience in AWS services. With the rise of data-driven decision-making and the increased availability of data, the demand for machine learning professionals is only going to increase.
- Variety of Opportunities: Machine learning is being used in a variety of industries, from healthcare to finance to entertainment. This means that there are a variety of opportunities to work in different domains and industries.
- Exciting Challenges: Working in machine learning involves solving complex problems and developing innovative solutions. This can be both challenging and rewarding, as you work to develop models that can make a real difference.
- Career Growth: A career in machine learning can provide opportunities for growth and advancement. As you gain experience and expertise, you may be able to take on more challenging projects, lead teams, or even start your own business.
- Competitive Salary: Machine learning specialists with experience in AWS services are in high demand, and this demand translates into competitive salaries.
With the growing demand for machine learning professionals and the variety of opportunities available, now is a great time to explore this field and see where it can take you.
