AWS Certified Machine Learning: Jobs and Career Opportunities

AWS has revolutionized the industry by providing IT courses that genuinely improve a person’s skill set and assist them in becoming a better version of themselves. It will be beneficial for you to pursue one or more of the certifications that AWS provides if you want to specialize in a certain area. These days, there is a great demand for the many certifications that AWS offers, and many people choose to seek both associate and professional-level credentials. One such certification that verifies a candidate’s proficiency in machine learning is the AWS Certified Machine Learning Specialist.

The timing is right for you to move forward with this role of the AWS Certified Machine Learning Specialist if you have experience with machine learning, also referred to as deep learning.

Career Flexibility: AWS Machine Learning Specialty Role

Your expertise in using AWS to build machine learning solutions to address business challenges will be validated by the AWS Machine Learning specialty-level position. Due to its specialty-level categorization, it is not included in the Foundational, Associate, or Professional categories that AWS uses to classify the majority of its certifications.

AWS Machine Learning, though, is unquestionably worthwhile. This demanding and specialized position requires an understanding of some of the most cutting-edge facets of building machine-learning solutions and putting those ideas into use using AWS. Consider pursuing an alternative machine learning certification if you’re positive that you’ll never utilize AWS in your machine learning career.

Otherwise, AWS Machine Learning will be a good role if you know you’ll be working deeply with big data, AI, and machine learning. As a result, various career paths can be opened when you have the title of machine learning specialist.

Tasks of an AWS Machine Learning Specialist:

AWS Certified Machine Learning Specialty professionals are capable of carrying out tasks in both data science and development roles. They are proficient in creating, designing, or managing machine learning/deep learning workloads in the AWS Cloud. They are also accountable for:

• ML or deep learning workload development, architecture, and operation on the AWS Cloud.
• Describing the core ML algorithms’ underlying principles.
• Fundamental optimization of hyperparameters.
• Using machine learning and deep learning frameworks.
• Adopting best practices for deployment, training, and operations.
• Preparing, examining, and finding trends in historical data.
• Offering technical assistance for program management and business development tasks including proposal preparation and client cultivation.

AWS Machine Learning Specialty Role: Salary

• With less than two years of experience to 11 years of experience, the compensation of a machine learning specialist in India ranges from ₹ 3.7 lakhs to ₹ 34.2 lakhs, with an average yearly income of ₹ 13.2 lakhs. In simple words:
  • An Entry Level Machine Learning Specialist makes an average income of ₹ 6.2 Lakhs a year with fewer than three years of experience.
  • An experienced machine learning specialist in the middle of their career may expect to make an average of ₹13.2 lakhs annually.
  • Whereas the average yearly compensation for a machine learning specialist with 10 to 20 years of expertise is ₹ 19.6 lakhs.
• In the USA, the average compensation for a machine learning expert is $150,563 per year or $77.21 per hour. Most experienced professionals earn up to $229,500 per year, while entry-level roles start at $97,500.

Top Companies Salary

Machine Learning Specialist: Related and Trending Job Roles

– Data Scientist

A data scientist is a specialist who gathers massive data sets using analytical, statistical, and programming abilities. They create data-driven solutions that are specifically adapted to an organization’s requirements. They are responsible for:

• Locating useful data sources and automating the data-collecting procedure.
• Preprocessing data, both organized and unstructured.
• Analyzing a big volume of data to find patterns and trends.
• Making machine learning and predictive modeling algorithms.
• Using ensemble modeling, combine models.
• Utilizing data visualization tools to provide information.
• Offering tactics and answers to corporate problems.
• Working together with the product development and engineering teams.

Salary: Data scientists make an average of $100,560 per year.

– Cloud Engineer

IT professionals in a cloud engineer’s role create, deploy, and maintain cloud-based commercial solutions. They design and execute cloud-based applications, convert on-premises programs to the cloud, and troubleshoot cloud stacks. They are responsible for:

• Evaluating and identifying the best cloud solutions in cooperation with the engineering and development teams.
• Educating teams on the use of fresh cloud efforts and technology.
• Creating, creating, and implementing modular cloud-based applications.
• Using best practices for creating and managing cloud systems.
• Ensuring that data processing and storage operations are carried out effectively and in compliance with corporate security guidelines and industry best practices.
• Locating, evaluating, and fixing infrastructure vulnerabilities and deployment problems with applications.
• Regularly evaluating current systems and offering suggestions for enhancements.
• Interacting with customers, offering cloud support, and providing advice depending on customer requirements.

Salary: A Cloud Engineer makes, on average, $123031 in a year.

– Cloud Architects

The professionals in charge of managing a company’s cloud computing infrastructure are known as cloud architects. They work on cloud application designs, cloud approval strategies, and cloud storage management systems. Further, they are responsible for:

• Managing the adaptation process and developing a knowledgeable cloud strategy.
• Reviewing cloud software, hardware, and apps often.
• Creating and setting up cloud computing systems.
• Monitoring the company’s cloud privacy closely in conjunction with IT security.
• Responding to technical problems in a timely and competent manner.
• Offering advice on methods for moving infrastructure, such as mass transferring of applications to the cloud.
• Effectively satisfying the strategic goals of the firm and identifying the best cloud architecture solutions.

Salary: A Cloud Architect makes, on average, $122137 annually.

– ML DevOps Engineer

DevOps engineers in machine learning (ML) are in charge of creating and implementing ML-related apps and pipelines for a range of practical issues in many business areas. They collaborate closely with the research team to launch ML prototypes. By often exchanging information with other senior employees or working with outside specialists, they will develop their skills. Further, they are responsible for:

• Developing technical solutions for MLOps and ML application development projects, and then managing such projects to meet the set goals.
• Enlisting the help of ML engineers to integrate ML prototypes into live applications.
• Providing support and help with troubleshooting for the ML pipelines.
• ML infrastructure management (our server system).

Salary: In the US, an MLOps engineer makes an average income of about $100K.

– Machine Learning Engineer

To automate predictive models for suggested searches, virtual assistants, translation applications, chatbots, and autonomous automobiles, machine learning engineers create self-running AI software. They create machine learning systems, use algorithms to make precise predictions, and fix issues with data sets. Further, they are responsible for:

• Investigating and modifying data science prototypes.
• Creating systems for machine learning.
• Finding the best ML tools and algorithms, then using them.
• Creating machine learning applications in accordance with specifications.
• Choosing the best datasets and data representation techniques.
• Running tests and experiments using machine learning.
• Utilizing test findings to do statistical analysis and fine-tuning.
• Training and upgrading systems.
• Extending current ML frameworks and libraries.

Salary: In the US, a machine learning engineer makes an average income of $145,542.

– Data Engineer

A data engineer is a person with skills in both data engineering and programming who develops systems to gather, organize and transform raw data into information that business analysts can utilize. They are responsible for:

• Arranging and analyzing raw data.
• Systematizing and streamlining your data.
• Analyzing the requirements and goals of your firm.
• Understanding patterns and trends.
• Conducting a detailed data analysis and presenting findings.
• Creating the data necessary for predictive and prescriptive modeling.
• Developing prototypes of algorithms.
• Combining the raw data from several sources.
• Examining strategies to improve the accuracy and dependability of the data.
• Determining data-acquisition opportunities.
• Creating programs and analytical tools.
• Working on various projects in collaboration with data scientists and architects.

Salary: Data engineers make an average income of $93572.

– Application Architect

Applications are designed and developed under the supervision of application architects. Together with internal stakeholders and application development teams, they work on application design, execute and track application development phases, and record application development processes. They are responsible for:

• Determining the requirements for business-specific applications in collaboration with top management.
• Putting together and carrying out application development strategies for fresh or current apps.
• Overseeing the design, testing, and modification phases while leading the team that develops applications.
• Showing off application prototypes and incorporating user input.
• Application installation and upgrading, as well as writing scripts and code.
• Offering technical assistance to end users and mentoring young application developers.
• Executing debugging techniques and diagnostic checks.
• Preserving the standards, guidelines, and practices of application development.
• Incorporating current application architectural trends into development initiatives for applications.

Salary: An applications architect’s annual income in the United States is $124,900.

Things to focus on as an AWS Certified Machine Learning Specialist:

• Improve your ability to use the AWS Cloud to develop, construct, deploy, optimize, train, optimize, and manage ML solutions for specific business challenges.
• Work on comprehending software-based systems and developing automation tools to increase those systems’ operational effectiveness. Practical expertise in Python, R, Java, and SQL are some of the software skills.
• Recognize how to work with enormous datasets and where to look for valuable value in data. Develop your database management skills so you can present and save data to the machine learning system. Handling SQL and non-SQL databases as well as big data technologies (Hadoop) are among the skills.
• Recognize the principles underpinning fundamental ML algorithms.
• Become proficient in applying fundamental hyperparameter optimization, ML and deep learning frameworks, and best practices for model training.
• Identify and resolve the mathematical and statistical basis of AI and machine learning.
• Improve your communication abilities to convert the needs of businesses into guidelines for machine learning systems. You should be able to plan and oversee system installation initiatives.
• Practice your collaboration and leadership skills.

Prepare for Interview

Look at the most typical AWS Certified Machine Learning interview questions if you are well-prepared and wish to ace interviews for various Machine Learning positions. You should really think about changing your mind. Using these questions will still give you a better idea of what to anticipate in interviews.