Machine learning certifications help learners and professionals validate their knowledge of machine learning concepts, tools and platforms. They range from beginner-friendly programs to professional certifications focused on cloud and production ML.
1. Google Professional Machine Learning Engineer
The Google Professional Machine Learning Engineer certification validates skills in designing, building, deploying and managing machine learning solutions on Google Cloud.
- Level: Professional
- Prerequisites: No formal prerequisites; Google recommends 3+ years of industry experience including experience with Google Cloud.
- Covers ML model development, data and ML pipelines, model serving, MLOps, monitoring and generative AI solutions on Google Cloud.
2. AWS Certified Machine Learning: Specialty
The AWS Certified Machine Learning: Specialty validated expertise in building, training, tuning and deploying machine learning solutions on AWS. The certification was retired on March 31, 2026, so new candidates can no longer take the exam. Existing certifications remain active for three years from the date they were earned.
- Status: Retired on March 31, 2026
- Exam: 65 questions with a 180-minute duration and a $300 USD registration fee before retirement.
- Covered data engineering, exploratory data analysis, model development, model implementation and machine learning operations on AWS.
3. Microsoft Certified: Azure AI Engineer Associate
The Microsoft Certified: Azure AI Engineer Associate validated the ability to design and implement AI solutions using Azure services. The certification and its AI-102 exam were retired on June 30, 2026.
- Status: Retired on June 30, 2026
- Covered AI solution development using Azure AI services including generative AI, computer vision, language processing and multimodal AI.
- Microsoft replaced this certification with the Azure AI Apps and Agents Developer Associate certification for its newer AI application and agent development path.
4. Microsoft Certified: Azure Data Scientist Associate
The Microsoft Certified: Azure Data Scientist Associate focused on building, training, evaluating and deploying machine learning models using Azure Machine Learning. The certification and DP-100 exam were retired on June 1, 2026.
- Status: Retired on June 1, 2026
- Covered data preparation, model training, automated machine learning, hyperparameter tuning, pipelines and model deployment using Azure Machine Learning.
- Microsoft introduced the Machine Learning Operations (MLOps) Engineer Associate certification as a replacement path for the retiring credential.
5. Databricks Certified Machine Learning Associate
The Databricks Certified Machine Learning Associate validates foundational skills for performing machine learning tasks using Databricks Machine Learning.
- Level: Associate
- Prerequisites: No formal prerequisites; hands-on experience with Databricks machine learning workflows is recommended.
- Covers data exploration, feature engineering, model training and evaluation, ML workflows and model deployment. The exam includes multiple-choice or multiple-selection questions and is delivered online with a two-year certification validity.
6. Databricks Certified Machine Learning Professional
The Databricks Certified Machine Learning Professional is designed for professionals who build and manage advanced machine learning solutions in production using Databricks.
- Level: Professional
- Prerequisites: Experience with Databricks Machine Learning, ML workflows and production machine learning is recommended.
- Covers scalable ML pipelines, distributed training, hyperparameter tuning, advanced MLflow capabilities, feature engineering, MLOps, automated retraining and model monitoring.
7. Machine Learning Specialization (DeepLearning.AI / Stanford)
The Machine Learning Specialization is a beginner-friendly program created in collaboration between DeepLearning.AI and Stanford Online. It provides a foundation in modern machine learning through three courses.
- Level: Beginner
- Duration: About 2 months at 10 hours per week, depending on learning pace.
- Covers supervised learning, neural networks, decision trees, unsupervised learning, recommender systems, reinforcement learning and practical ML development using Python, NumPy, scikit-learn and TensorFlow.
8. TensorFlow Developer Certificate
The TensorFlow Developer Certificate was designed to validate practical skills in building machine learning and deep learning models using TensorFlow. The TensorFlow certification exam is currently closed while TensorFlow evaluates the next step for its certificate program.
- Status: Exam currently closed
- Focused on developing and training models for areas such as computer vision, natural language processing and time-series problems using TensorFlow.
- Credentials earned by candidates who passed the exam remain valid for three years from the date they were earned.
9. IBM Machine Learning Professional Certificate
The IBM Machine Learning Professional Certificate is an intermediate-level program that develops practical machine learning skills through hands-on coursework and projects.
- Level: Intermediate
- Duration: About 3 months at 10 hours per week, depending on learning pace.
- Covers supervised and unsupervised learning, feature engineering, deep learning, dimensionality reduction, recommender systems, time-series forecasting and reinforcement learning using Python and scikit-learn.
10. IBM AI Engineering Professional Certificate
The IBM AI Engineering Professional Certificate develops practical skills for building and deploying machine learning and AI solutions.
- Level: Intermediate
- Duration: About 4 months at 10 hours per week, depending on learning pace.
- Covers machine learning, deep learning, neural networks, computer vision, generative AI and model deployment using tools such as scikit-learn, PyTorch, TensorFlow, Keras and Apache Spark.
