Cloud adoption and utilization of data science are on the rise. Machine learning, also called ML, is a broad topic with many applications across industries. With it, applications are more accurate at predicting outcomes without being programmed to. Thus, attaining a machine learning certification is a great step to further or change careers.
A few of the most popular machine learning certifications are from cloud vendors, such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud. Here, we provide an overview of:
- AWS Certified Machine Learning - Specialty
- Azure Data Scientist Associate
- Google Cloud Professional ML Engineer Certification
Learn what to expect and the recommended study tips and materials to prepare for each exam.
AWS Certified Machine Learning - Specialty
The AWS Certified Machine Learning - Specialty exam covers four broad categories:
- data engineering
- exploratory data analysis
- data modeling
- machine learning implementation and operations
Exam takers should be familiar with topic such as data ingestion and transformation concepts, 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, have at least two years of experience developing and running machine learning workloads on AWS.
A great way to get experience is through Amazon's machine learning training classes. It offers two free ML-related classes: Process Model: CRISP-DM on the AWS Stack and The Elements of Data Science. On top of this, consider three paid classes from the cloud provider:
- Practical Data Science with Amazon SageMaker
- Deep Learning on AWS
- The Machine Learning Pipeline on AWS
Amazon's machine learning 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.
To supplement the learning further, AWS Certified Machine Learning Specialty 2021 is a well-reviewed Udemy course that covers modeling, the AWS SageMaker ML platform, feature engineering and more. These topics appear on the AWS exam and are worth reviewing.
Azure Data Scientist Associate
Microsoft's Azure Data Scientist Associate certification is the most beginner-friendly of the certifications covered here. Microsoft expects test takers to have working knowledge of how to implement and run machine learning models on the Azure cloud platform, train predictive models and use the Azure Databricks analytics platform.
Microsoft is transparent about how the DP-100 exam dives into each general topic:
- 25-30% on managing Azure resources for machine learning;
- 20-25% on running experiments and training data models;
- 35-40% on deploying and managing machine learning products; and
- 10% on implementation of responsible machine learning practices.
Microsoft offers four free learning paths that together cover a lot of the exam's subject matter:
- Create machine learning models
- Microsoft Azure AI Fundamentals
- Build and operate machine learning solutions with Azure Machine Learning
- Build and operate machine learning solutions with Azure Databricks
These courses vary in length from three to 10 hours. For even more study material, Udemy hosts the DP-100 Microsoft Azure Data Scientist Complete Exam Prep course.
Azure's certification exam is between 40 and 60 questions, lasts 100 minutes and costs $165.
Consider Coursera's full Machine Learning Specialization track. It has a curated list of four complete courses to take. These courses go in-depth into important machine learning practices and concepts. Sign up for this track only if you have prior experience in machine learning.
Google Cloud Professional ML Engineer certification
Google Cloud Professional ML Engineer certification covers six primary categories:
- framing ML problems
- creating ML solutions
- designing data preparation and processing systems
- developing ML models
- automating and orchestrating ML pipelines
- monitoring, optimizing and maintaining ML solutions
Google recommends at least three years of hands-on experience with its cloud platform before taking the exam. But Google does provide a recommended learning path for this certification to get up to speed on machine learning with its cloud platform. Google's Machine Learning and Artificial Intelligence path begins with the fundamentals of big data and machine learning. Then, it progresses into topics such as Google's ML platform TensorFlow, MLOps automation frameworks and ML pipelines.
The Google Cloud Professional ML Engineer certification exam can be taken remotely or at a local testing center. It is two hours long and costs $200.
Other preparation material includes a course, Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate, made by Google, which you can find on Coursera. The Google Cloud Professional Data Engineer: Get Certified course on Udemy also teaches machine learning and data pipelines topics covered by the exam.