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5 Certifications to Validate Your Machine Learning Skills (+ Training)

September 12, 2024 | Activate Learning | 6 min read

Artificial intelligence has sharply risen in demand as more and more companies strive to launch projects reliant on the technology. However, a gap remains in the market.

There aren't enough skilled professionals available to keep up with demand, making roles like machine learning specialists highly sought after. Companies are vying for these professionals who know AI and its many branches.

Certifications make prospective candidates especially valuable to recruiters and hiring managers. They want professionals who have third-party validation of their skills, and they're willing to pay extra for the assurance of their capabilities.

The certifications below help candidates stand out in the market because they validate a professional's skills in the respective areas.

Learn more about each, what they pay, and how to earn them.

Popular Certifications for Machine Learning Specialists

Google Cloud – Professional Machine Learning Engineer

A certified Professional Machine Learning Engineer uses Google Cloud technologies to build, evaluate, and optimize machine learning models. They handle large datasets, create reusable code, and integrate responsible AI practices.

Professionals with these titles often work collaboratively to make AI/ML accessible throughout their organization and ensure project success. Much of this certification focuses on scaling and improving ML solutions over time.

Professionals must know model architecture, pipeline creation, and metrics interpretation to earn this certification. "Automating and orchestrating ML pipelines" counts for 21% of the current exam, testing professionals' ability to develop pipelines, automate training, and more. Further, a basic understanding of MLOps, application development and data governance will help come exam day.

The exam will also test professionals' ability to create low-code solutions, manage data and models, scale prototypes, and automate ML pipelines. While coding isn't directly tested, strong programming skills and experience with data platforms help. Basic proficiency in Python and Cloud SQL is necessary to interpret code snippets.

*Note: a new version of the exam launches Oct. 1, 2024, which will include more about working with and developing generative AI solutions.

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The Exam

Topic%
Architecting low-code ML solutions ~12%
Collaborating within and across teams to manage data and models ~16%
Scaling prototypes into ML models ~18%
Serving and scaling models ~19%
Automating and orchestrating ML pipelines ~21%
Monitoring ML solutions ~14%

AWS Certified – Machine Learning Specialty

The AWS Certified Machine Learning - Specialty credential is meant for those who will develop — from idea to deployment — machine learning models on AWS.

This certification confirms expertise in creating, training, optimizing, and deploying ML models using AWS. It's aimed at individuals in development or data science roles with over a year of experience managing machine learning/deep learning workloads on AWS.

Candidates should have two years of hands-on experience with ML on AWS, a grasp of fundamental ML algorithms, hyperparameter tuning skills, and familiarity with ML frameworks. The exam's largest portion, 36%, is devoted to modeling. This domain tests a candidate's ability to align business challenges with ML and then develop and optimize a solution.

To earn the certification, one must pass the current exam, which includes multiple choice and response questions. Further details, like the exam outline and passing criteria, are available in the exam guide.

  • Examcost: $300 USD
  • Global Average Salary: $136,595
  • Training: AWS Skill Builder (from AWS)

The Exam

Topic%
Data Engineering 20%
Exploratory Data Analysis 24%
Modeling 36%
Machine Learning Implementation and Operations 20%

Microsoft Certified: Azure AI Fundamentals

The Azure AI Fundamentals certification provides an opportunity to showcase your understanding of machine learning, AI concepts, and associated Microsoft Azure services.

The certification doesn't require a technical background, making it applicable to virtually anyone who's interested in AI and machine learning. This credential proves that professionals understand the fundamentals of this technology and how Microsoft Azure supports the development of AI solutions. Describing the fundamentals of machine learning accounts for the greatest portion of the exam.

Again, no prior experience in data science or software is required. Although, Microsoft recommends having some knowledge of cloud concepts and client-server applications. Training for this certification would also benefit those who plan to pursue others, like the Azure AI Engineer Associate (listed below).

For those still in college, passing the current certification exam may qualify you for credit. The skills assessed include understanding AI workloads and considerations, machine learning principles on Azure, and features of computer vision, Natural Language Processing (NLP), and generative AI workloads on Azure.

The Exam

Topic%
Describe Artificial Intelligence workloads and considerations 15–20%
Describe fundamental principles of machine learning on Azure 20–25%
Describe features of computer vision workloads on Azure 15–20%
Describe features of Natural Language Processing (NLP) workloads on Azure 15–20%
Describe features of generative AI workloads on Azure 15–20%

Microsoft Certified: Azure AI Engineer Associate

The Azure AI Engineer certification validates a professional's ability to build and implement AI solutions on the platform, doing so securely and collaboratively. These professionals often work with others to conceptualize and develop the solution, and then deploy and integrate it.

Knowing this, these professionals should know programming languages like Python or C#, experience with APIs and SDKs. Naturally, these professionals must also know Azure and its capabilities. Unlike the certification mentioned earlier, this is best suited to those at an "intermediate" level in their careers, planning to pursue a role as an AI engineer, according to Microsoft.

To pass the exam, professionals must demonstrate their ability to plan and manage AI solutions on Azure, including content moderation, computer vision, natural language processing (this is most heavily tested), knowledge mining and document intelligence, and generative AI.

The Exam

Topic%
Plan and manage an Azure AI solution 15–20%
Implement content moderation solutions 10–15%
Implement computer vision solutions 15–20%
Implement natural language processing solutions 30–35%
Implement knowledge mining and document intelligence solutions 10–15%
Implement generative AI solutions 10–15%

IBM Certified Data Scientist – Machine Learning Specialist

For this certification, professionals must know how to respond to business challenges with ethically sound solutions. This includes knowing when and how to implement the appropriate model.

Professionals who hope to unlock this certification must know how to use Watson Studio and other IBM AI solutions to solve business challenges through exploratory data analysis and preparation, model implementation, and maintenance.

This is an advanced-level certification, and the bulk of the training offered by IBM for it is meant for intermediate- and advanced-level professionals. Most of it covers supervised learning (regression and classification), unsupervised machine learning, and deep learning.

The Exam

Topic%
Evaluate business problem including ethical implications 21%
Exploratory Data Analysis including data preparation 18%
Implement the proper model 26%
Refine and deploy the model 18%
Monitor models in production 17%

Aspiring to a Career in AI? This Training Will Help

Given the popularity of AI and the potential of the technology, there's more appetite to hire professionals with these skills — or train those who show an interest and aptitude for the work.

Employers that have professionals with these credentials on staff retain a competitive advantage over others in the market, which will ultimately help them win business, satisfy client requirements, and ensure in-house capability to get projects off the ground.

For individuals, these credentials naturally appeal to hiring managers, signaling credibility and third-party validation.  

But where should you start?

Codecademy offers a Career Path for aspiring machine learning specialists. Not only does it earn those who complete the path a certification, but it progresses learners through 59 hands-on projects, tests their knowledge, and exposes them to Python, SQL and algorithms.

Best of all, it's meant for anyone interested in learning the trade. Check out the syllabus to learn what it entails.

SEE CAREER PATH

How We Built This List

This list of machine learning certifications is based on survey responses from Skillsoft's IT Skills and Salary Survey conducted from May to September 2023. The survey asks respondents about their current jobs and experience, certifications and salaries, and more. Respondents encounter multiple choice and multi-select, open-ended, rank choice, and other types of questions while taking the survey. Technology providers, certification bodies, and Skillsoft distribute the survey to IT professionals worldwide.

When reporting salary figures, Skillsoft looks for at least 50 survey responses before considering relevance, demand, and other factors. Salaries are not normalized for cost of living or location. Salaries denoted with an asterisk (*) fall below the 50-response threshold, presented only for continuity and not statistical relevance.