The adoption of the cloud and the use of data are increasing, and automatic learning is a key element in the way the data is used, with many Applications in various industries. With it, applications can predict the results more precisely without in -depth programming. For data science engineers, an automatic learning certification is essential; It is also an excellent step for other IT professionals to continue or change their careers.
ML certifications Serving as evidence that a technology professional has achieved an official exam that properly tested his knowledge of the subject. Some of the most popular ML certifications come from cloud suppliers, such as AWS, Google, IBM and Microsoft. The certifications of these sellers include the following elements:
- AWS Certified Machine Learning – Specialty.
- Google Cloud Professional Automatic Learning Engineer.
- IBM Machine Learning Professional Certificate.
- Microsoft certified: Azure Data Scientist Associate.
Each certification requires a different study program and generally an examination. Sellers provide information on what to expect and recommend advice and study materials to prepare each exam.
Why is an automatic learning certificate important?
ML expertise is in demand, but it is not exactly easy to be hired like a ML engineer or specialist given the specificity of the skills required for today's jobs. Having an ML certificate guarantees that you have controlled the crucial skills that jobs on this market need.
Data scientists, data analysts, software developers and others working in related areas will also find the skills obtained from these programs. Given how AI and ML have exploded in use and popularity in recent years, these technologies have appeared in IT environments. The skills in AI and ML will not become obsolete as soon as, and experts in the ML industry will continue to be necessary.
Certifications offer the following advantages:
- Proof of the expertise of an IT professional.
- A practical experience regarding real world problems using automatic learning tools and data sets that imitate real world experiences.
- Update information and skills for a constantly evolving field because ML certification courses are regularly updated.
AWS Certified Machine Learning – Specialty
AWS Certified Machine Learning – Specialized exams cover four areas of expertise:
- Data engineering. Tasks include the creation of data benchmarks for ML and the identification and implementation of data ingestion and data processing systems.
- Analysis of exploratory data. Tasks include disinfection and data preparation for modeling, realization of functionalities engineering and data analysis and visualization for ML.
- Modeling. Tasks include framing commercial problems as ML problems, selection of appropriate models for a given ML problem, training and evaluation of ML models and optimization of hyperparameter.
- Implementation and operations of automatic learning. Tasks include ML construction offers for performance, availability, scalability, resilience and defect tolerance; Recommend and implement the appropriate ML services and features for a given problem; Apply basic AWS security practices to ML systems; and the deployment and operationalization of ML systems.
Examination candidates should be familiar with subjects such as ingestion and processing of data, data cleaning, data visualization, transformation of commercial problems into automatic learning problems, training ML models and implementation of ML services in AWS. To prepare for the exam, candidates should have at least two years of experience in the development and management of automatic learning workloads on AWS.
Amazon ML certification examination Takes three hours, includes 65 questions and costs $ 300. The test is available as an online pro -line or in person in a test center.
Additional resources
To further complete learning, AWS Certified Machine Learning Specialty 2025 – Practical! is a Well -revised Udemy Courses This covers subjects such as modeling, the Amazon Sagemaker ML platform and functionalities engineering. These subjects appear on the AWS exam and deserve to be examined.
Google Cloud Professional Machine learning engineer
Google Cloud Professional Machine Learning Engineer covers six main categories:
- Architecting AI implementations with low code.
- Collaboration within and between teams to manage data and models.
- Land of prototypes in ML models.
- Service and scale models.
- Automatize and orchestrate ML pipelines.
- Monitoring of AI systems.
Google recommends at least three years of experience in industry and one year of practical experience with its cloud platform before taking the exam. It provides a recommended apprenticeship path for this certification to get aware of automatic learning with its cloud platform. Google data scientist and automatic learning engineer Learning path Begins with the fundamental principles of Big Data and automatic learning. Then he progresses in subjects such as Google's ML platform, Tensorflow; Fundamentals of automatic learning operations; and ML pipelines.
Google Cloud's Professional Machine Learning Engineer certification exam can be taken at a distance or in a local test center. It lasts two hours and costs $ 200 with questions of 50 to 60 multiple and multiple choices.
Additional resources
Another preparation equipment includes the Google course Preparation for Google Cloud certification: Professional certificate of the Automatic Learning Engineer, supplied by racera.
IBM Machine Learning Professional Certificate
The Professional IBM Automatic learning certificate program covers four major areas: strengthening learning, Deep Learning, Supervised learning and not monitored learning. IBM also recommends having basic computer skills, as well as knowledge of linear algebra, statistics and Python programming.
IBM offers the following Six lessons on Racera that candidates must pass To win the certificate::
- Analysis of exploratory data for automatic learning.
- Supervised automatic learning: regression.
- Supervised automatic learning: classification.
- Automatic learning not supervised.
- In -depth learning and learning strengthening.
- Automatic learning capstone.
Additional resources
In addition to having the knowledge recommended by IBM of certain areas before starting these courses, there is no prerequisite or programming experience required. However, IBM offers a useful learning path with Various training assets To test your skills in ML at the associated, professional and advanced levels.
Microsoft certified: Azure Data Scientist Associate
The Azure Data Scientist Associate certification is the most respectful of the certifications covered here. The examination required to obtain this certification is called DP-100: Design and implement a solution of data science on Azure.
Microsoft expects candidates to have practical knowledge of how to implement and run automatic learning models on the Azure Cloud platform as well as MLFLOW.
Microsoft is transparent on how the DP-100 examination plunges into each general subject:
- 20% -25% on the design and preparation of an automatic learning solution.
- 35% -40% on the exploration of data and training models.
- 20% -25% on the preparation of a deployment model.
- 10% -15% on the deployment and recycling of a model.
Microsoft Offer Six learning paths at your own pace which cover a large part of the subject of the exam together:
- Design an automatic learning solution.
- Explore and configure the Azure Machine Learning workspace.
- Experience with Azure Automatic Learning.
- Optimize model training with Azure automatic learning.
- Manage and examine models in Azure automatic learning.
- Deploy and consume models with Azure automatic learning.
These courses vary for one to four hours. Users can also take a Course led by the instructor Entitled the design and implementation of a data science solution on Azure.
Azure certification exam lasts 100 minutes and costs $ 165.
Additional resources
Applicants can also consider Specialization of automatic learning track. It has an organized list of four courses which deepens in -depth of important ML practices and concepts. Register for this track only if you have previous experience on automatic learning.
Publisher's note: The courses in this part were the search result using Google Trends. It was updated in December 2025 to reflect the evolution of course criteria and improve the reader's experience.
Kaitlin Herbert is content editor and former editor -in -chief of the techtarget learning content team. She writes definitions and features.
Cameron Hachemi-Pour is a technical and writer at Techtarget.
Dan Sullivan, M.SC., is an author, systems architect and consultant with more than 20 years of IT experience with commitments in advanced analysis, systems architecture, the design of the database, business security and business intelligence. He worked in a range of industries, including financial services, manufacturing, pharmaceutical products, software development, government, retail, electricity production and education.
At Learnopoly, Finn has championed a mission to deliver unbiased, in-depth reviews of online courses that empower learners to make well-informed decisions. With over a decade of experience in financial services, he has honed his expertise in strategic partnerships and business development, cultivating both a sharp analytical perspective and a collaborative spirit. A lifelong learner, Finn’s commitment to creating a trusted guide for online education was ignited by a frustrating encounter with biased course reviews.