Machine Learning Skills

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Machine Learning Skills

Certified Cloud Practitioners, also known as AWS Certified Cloud Practitioners, work with Amazon Web Services (AWS). These individuals have demonstrated knowledge and understanding of the AWS Cloud technology. Most of these practitioners are in technical, sales, purchasing, managerial, or financial roles that utilize AWS Cloud. Marking for this position requires understanding of AWS Cloud, qualifications of practitioners, how the career fits into machine learning, and an understanding of future opportunities and trends associated with this career.

AWS Cloud

  • The AWS Cloud is a global technology platform with more than 175 services available.
  • Multiple business populations, from startups to enterprises, use AWS to increase innovation, flexibility, and cost efficiency in their businesses.
  • AWS reports millions of consumers are using this technology today.

Qualifications of an AWS Cloud Practitioner

  • AWS Cloud Practitioners are required to pass a certification test to attain this title. This test determines the amount of skills and abilities the applicant possesses regarding AWS.
  • Certification attainment requires knowledge and application of the AWS platform in various uses, understanding of billing and account management, knowledge of the AWS security model, and basic understanding and ability to use the core characteristics of the AWS platform.
  • An AWS Cloud Practitioner also must demonstrate understanding of the principles and values of the AWS platform, the global infrastructure involved, and ways to obtain technical assistance when needed.
  • Additional recommendations prior to certification include six months of experience with the AWS Cloud and a basic understanding of how IT services are used in the platform.
  • On average, AWS Certified Cloud Practitioners make $130,866 annually, though the salary range is quite vast depending on the practitioner's specialty.

Machine Learning and AI Connections

  • The AWS platform offers more than 65 machine learning courses for businesses to use for training.
  • Multiple customer types use this training, such as developers, data platform engineers, data scientists, and decision-makers.
  • AWS offers an additional credential on top of certification, which is the certified machine learning specialty. This specialty allows practitioners to develop, utilize, and maintain machine learning initiatives for businesses.
  • AWS uses machine learning and artificial intelligence (AI) to offer business solutions for complex problems, allowing businesses to find innovative and effective solutions for success.
  • AWS Cloud Practitioners may work with Amazon SageMaker, which builds machine learning models, trains on these models, and initiates use of the models.
  • The AWS Cloud Practitioner certification is an initial step towards multiple specialty certifications.
  • Job Market Trends and Opportunities

  • AWS Certified Cloud Practitioners can proceed to multiple AWS specialties that use machine learning skills, such as cloud architect, cloud developer, cloud develops engineer, software engineer, networking specialist, and big data analyst.
  • Obtaining an AWS certification, such as the many cloud specialties available, is highly recommended for an individual seeking recognition as an expert, which opens career doors.
  • Professions who obtained AWS certifications have reported a 20% increase in salary as a result of certification.
  • AWS certification was noted as one of the most valuable certifications in IT for 2019.
  • The continuing shortage of IT skills in the workforce, most significantly in machine learning and AI, have led to an ongoing need for AWS certification in the workforce.
  • The AWS Certified Cloud Practitioner credential opens doors for the professional to work in multiple AWS areas, such as architect, development, and operational venues, each area with additional specialty certifications available, as seen in the figure below. This skill set allows the professional to remain relevant in the field, providing strong job security.

Machine learning engineers are in high demand in today's society. This career was rated the best job of 2019, with demand expected to continue growing. Marketing this career requires knowledge of specific qualifications, understanding of how the career is linked to innovations in machine learning and AI, and overall conceptualization of trends in the job market.

Qualifications and Job Duties

  • Machine learning (ML) engineers develop and design machine and deep learning systems.
  • ML engineers run experiments and tests with machine learning technology and implement the appropriate algorithms.
  • ML engineer qualifications include experience with machine learning, understanding of data structure and modeling, software knowledge, multiple framework understanding, and strong analytical skills.
  • ML engineers also need strong communication and teamwork skills and at least a bachelor's degree in a related field.

Machine Learning and AI Connections

  • A recent survey of managers in the United States found 30% of companies use AI and machine learning technology.
  • Additionally, 53% of surveyed managers reported a plan to incorporate AI and machine learning in their businesses within 3-5 years (2022-2025).
  • Businesses need more experts in machine learning, such as ML engineers, to achieve the AI and machine learning business goals.
  • ML engineers are able to design automatic processes and self-running software programs, which are ideal for business innovation and efficiency.
  • ML engineers are essential in the development of AI processes to keep up with current and future technology shifts.

Machine Learning Engineer Trends

  • ML engineers were rated as the top job in 2019, showing a 344% growth increase from 2018-2019. As companies continue implementing AI and machine learning technology in business, it is expected the demand for ML engineers will continue growing at an accelerated rate.
  • The versatility of machine learning is just beginning to be recognized and utilized, meaning the demand for ML engineers will continue to rise.
  • In 2018, machine learning was the skill in the highest demand in relation to AI, and this trend continues.
  • Multiple applications of machine learning are used in business, such as speech and image recognition, risk management, and customer insight.
  • ML engineers bridge the gap from technology idea to reality, keeping this a highly-demanded profession.


Computer Vision Engineers use research data to solve problems, allowing artificial intelligence to develop the ability to mimic human sight. These engineers research identified problems and develop machine learning interventions along with computer vision to assist the client with solutions. These engineers practice in multiple industries, such as medical, manufacturing, defense, and monitoring. Marketing to attract these professionals requires an understanding of necessary qualifications, how these engineers fit into the machine learning and AI world, and current and future trends for the profession.

Qualifications

  • Computer vision engineers need a minimum of a bachelor's degree in a computer science field.
  • Typical skills required include knowledge of computer vision libraries, software skills such as data base management and object oriented software.
  • Additional skills needed for this career include critical thinking, logical thinking, reasoning skills, being detail-oriented, and analytical problem-solving.
  • Job postings for computer vision engineers tend to list additional qualifications such as experience with specific machine learning tools, large amounts of data, organizational skills, and other job-specific experience.

Machine learning and/or AI Connections

  • Machine learning and computer vision are connected. Machine learning has assisted computer vision development related to tracking and recognition processes.
  • The interpretation stage of computer vision uses machine learning, as does digital recording analysis.
  • Object recognition, detection, and identification are key portions of computer vision engineering. Machine learning and deep learning approaches are used in developing object recognition, depending on the needs of the project.
  • Various applications of computer vision utilize machine learning. Some of these applications include video tracking, autonomous vehicles, and various sports technologies.

Computer Vision Engineer Trends

  • The market growth for computer vision engineers is expected to continue at a rate of 19% through 2026.
  • Computer vision is considered the pathway to further humanizing artificial intelligence, and therefore this profession will continue being in demand as the AI market grows.
  • Computer vision technology will broaden in potential function in the future, as this technology becomes more refined and understood, creating additional opportunities and need for computer vision engineers.


Robotic Engineers are designers of robotic systems and robots. These engineers design systems that complete tasks that humans cannot accomplish or do not wish to do. Marketing jobs to these professionals requires understanding of job qualifications, the role of robotic engineers in machine learning and AI, and trends in the job market.

Qualifications and Job Duties

  • Robotic engineer job duties include configuring and testing robots, designing robotic systems and software to control the systems.
  • These engineers also serve as technical support for the robotic systems created and are expected to review costs associated with the robotic projects and teaching robotic technology.
  • Robotic engineers need a minimum education of a bachelor's degree in a field related to mechanical engineering, and positions beyond entry level require state licensure.
  • Robotics engineering positions tend to require skills in at least one of the following areas: computer aided drafting and design, building, and research and development.

Machine learning and AI Connection

  • Several AI and machine learning approaches apply to robotics. These involve engineers teaching robots vision, grasping, motion control, and understanding data patterns.
  • Machine learning can assist with object recognition, thereby enhancing robotic vision created by robotic engineers.
  • Robotics engineers use machine learning to teach the movement of grasping objects, teaching appropriate positioning for the grasp.
  • Motion control is developed by machine learning processes, teaching robots to interact with the environment and navigate obstacles.
  • Robotic engineers also use machine learning to interpret data patterns and make decisions without supervision.

Job Market Trends

  • Mechanical engineer jobs, of which robotics engineers are a portion, is expected to grow at a rate of 4% through 2028.
  • Trends specific to robotics engineering jobs show an estimated 9% growth rate through 2026.
  • Robotics engineers are essential in developing telepresence robots, which have audio and video capability and are able to be remotely operated. The market value for this technology and robots in 2018 was $148.3 million, with an expectation of a market value of $285.4 million by 2024, creating an additional niche of opportunity for robotics engineers.
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