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4 popular machine learning certificates to get in 2025

By Kaitlin Herbert

Cloud adoption and data use are on the rise, and machine learning is a key part of the way data is being used, with many applications across various industries. With it, applications can more accurately predict outcomes without extensive programming. For data science engineers, a machine learning certification is critical; it is also a great step for other IT professionals to further or change careers.

ML certifications serve as proof that a tech professional passed an official exam that adequately tested their knowledge of the subject. A few of the most popular ML certifications are from cloud vendors, such as AWS, Google, IBM and Microsoft. Certifications from these vendors include the following:

• AWS Certified Machine Learning -- Specialty.
• Google Cloud Professional Machine Learning Engineer.
• IBM Machine Learning Professional Certificate.
• Microsoft Certified: Azure Data Scientist Associate.

Each certification requires a different course of study and usually an exam. Vendors provide information on what to expect and recommended study tips and materials to prepare for each exam.

Why is a machine learning certificate important?

ML expertise is in demand, yet it's not exactly easy to get hired as an ML engineer or specialist given the specificity of skills required for today's jobs. Having an ML certificate ensures you've mastered the crucial skills that jobs in this market require.

Data scientists, data analysts, software developers and others working in related fields will find the skills obtained from these programs useful as well. Given how AI and ML have exploded in use and popularity over the last few years, these technologies are showing up throughout IT environments. AI and ML skills won't become obsolete anytime soon, and ML industry experts will continue to be needed.

Certifications provide the following advantages:

• Proof of an IT professional's expertise.
• Hands-on experience dealing with real-world problems using machine learning tools and data sets that emulate real-world experiences.
• Up-to-date insight and skills for an ever-changing field because ML certification courses are regularly updated.

AWS Certified Machine Learning -- Specialty

The AWS Certified Machine Learning -- Specialty exam covers four areas of expertise:

1. Data engineering. Tasks include creating data repositories for ML and identifying and implementing data ingestion and data transformation systems.
2. Exploratory data analysis. Tasks include sanitizing and preparing data for modeling, performing feature engineering, and analyzing and visualizing data for ML.
3. Modeling. Tasks include framing business problems as ML problems, selecting the appropriate models for a given ML problem, training and evaluating ML models and performing hyperparameter optimization.
4. Machine learning implementation and operations. Tasks include building ML offerings for performance, availability, scalability, resiliency and fault tolerance; recommending and implementing the appropriate ML services and features for a given problem; applying basic AWS security practices to ML systems; and deploying and operationalizing ML systems.

Exam takers should be familiar with topics such as data ingestion and transformation, data cleaning, data visualization, turning business problems into machine learning problems, training ML models and implementing ML services in AWS. To prepare for the exam, candidates should have at least two years of experience developing and running machine learning workloads on AWS.

Amazon's ML certification exam takes three hours, includes 65 questions and costs $300. The test is available as a proctored online exam or in person at a testing center.

Supplemental resources

To supplement the learning further, AWS Certified Machine Learning Specialty 2025 -- Hands On! is a well-reviewed Udemy course that covers subjects such as modeling, the Amazon SageMaker ML platform and feature engineering. These topics appear on the AWS exam and are worth reviewing.

Google Cloud Professional Machine Learning Engineer

The Google Cloud Professional Machine Learning Engineer certification covers six primary categories:

1. Architecting low-code AI implementations.
2. Collaborating within and across teams to manage data and models.
3. Scaling prototypes into ML models.
4. Serving and scaling models.
5. Automating and orchestrating ML pipelines.
6. Monitoring AI systems.

Google recommends at least three years of industry experience and one year of hands-on experience with its cloud platform before taking the exam. It provides a recommended learning path for this certification to get up to speed on machine learning with its cloud platform. Google's Data Scientist and Machine Learning Engineer learning path begins with the fundamentals of big data and machine learning. Then, it progresses into topics such as Google's ML platform, TensorFlow; machine learning operations fundamentals; and ML pipelines.

The Google Cloud Professional Machine Learning Engineer certification exam can be taken remotely or at a local testing center. It is two hours long and costs $200 with 50 to 60 multiple-choice and multiple-select questions.

Supplemental resources

Other preparation material includes the Google course Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate, provided by Coursera.

IBM Machine Learning Professional Certificate

IBM's Machine Learning Professional Certificate program covers four broad areas: reinforcement learning, deep learning, supervised learning and unsupervised learning. IBM also recommends having basic computer science skills, as well as knowledge of linear algebra, statistics and Python programming.

IBM offers the following six courses on Coursera that candidates must pass to earn the certificate:

1. Exploratory Data Analysis for Machine Learning.
2. Supervised Machine Learning: Regression.
3. Supervised Machine Learning: Classification.
4. Unsupervised Machine Learning.
5. Deep Learning and Reinforcement Learning.
6. Machine Learning Capstone.

Supplemental resources

Other than having IBM's recommended knowledge of certain areas before starting these courses, there aren't any prerequisites or required programming experience. However, IBM offers a useful learning path with various training assets to test your ML skills at the Associate, Professional and Advanced levels.

Microsoft Certified: Azure Data Scientist Associate

The Azure Data Scientist Associate certification is the most beginner-friendly of the certifications covered here. The exam required to get this certification is called DP-100: Designing and Implementing a Data Science Solution on Azure.

Microsoft expects test-takers to have working knowledge of how to implement and run machine learning models on the Azure cloud platform as well as MLflow.

Microsoft is transparent about how the DP-100 exam dives into each general topic:

• 20%-25% on designing and preparing a machine learning solution.
• 35%-40% on exploring data and training models.
• 20%-25% on preparing a model for deployment.
• 10%-15% on deploying and retraining a model.

Microsoft offers six self-paced learning paths that together cover a lot of the exam's subject matter:

1. Design a Machine Learning Solution.
2. Explore and Configure the Azure Machine Learning Workspace.
3. Experiment With Azure Machine Learning.
4. Optimize Model Training With Azure Machine Learning.
5. Manage and Review Models in Azure Machine Learning.
6. Deploy and Consume Models With Azure Machine Learning.

These courses vary in length from one to four hours. Users can also take an instructor-led course titled Designing and Implementing a Data Science Solution on Azure.

Azure's certification exam lasts 100 minutes and costs $165.

Supplemental resources

Candidates might also consider Coursera's full Machine Learning Specialization track. It has a curated list of four courses that go in depth into important ML practices and concepts. Sign up for this track only if you have prior experience in machine learning.

Editor's note: Courses in this piece were the result of research using Google Trends. It was updated in December 2024 to reflect changing course criteria and to improve the reader experience.

Kaitlin Herbert is a content writer and former managing editor for the Learning Content team at TechTarget. She writes definitions and features.

Cameron Hashemi-Pour is a technical editor and writer at TechTarget.

Dan Sullivan, M.Sc., is an author, systems architect and consultant with more than 20 years of IT experience with engagements in advanced analytics, systems architecture, database design, enterprise security and business intelligence. He has worked in a range of industries, including financial services, manufacturing, pharmaceuticals, software development, government, retail, power generation and education.