Cloud adoption and the use of data science are on the rise. Machine learning, also known as ML, is a huge topic with many applications in all industries. With it, applications are more accurate in predicting results without being programmed for. Thus, obtaining a certification in machine learning is a big step in advancing or changing careers.
Some of the most popular machine learning certifications come from cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Here we offer you an overview of:
- AWS Certified Machine Learning – Specialty
- Azure Data Scientist Associate
- Google Cloud Professional ML Engineer Certification
Find out 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
Candidates should be familiar with topics such as concepts of data ingestion and transformation, data cleansing, data visualization, transforming business problems into machine learning problems, training ML models and the implementation of ML services in AWS. To prepare for the exam, you must have at least two years of experience developing and running machine learning workloads on AWS.
Amazon’s machine learning training courses are a great way to gain experience. It offers two free courses related to ML: Process Model: CRISP-DM on the AWS Stack and The elements of data science. In addition to that, consider three paid courses from the cloud provider:
Amazon’s Machine Learning Certification Exam is three hours long, has 65 questions, and costs $ 300. The test is available as a proctored online exam or in person at a testing center.
To further complement learning, AWS Certified Machine Learning Specialty 2021 is a highly rated Udemy course that covers modeling, the AWS SageMaker ML platform, feature engineering and more. These topics appear on the AWS Exam and are worth considering.
Azure Data Scientist Associate
Microsoft’s Azure Data Scientist Associate certification is most suitable for beginners of the certifications covered here. Microsoft expects candidates to have practical 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 addresses each general topic:
- 25-30% on Azure Resource Management for Machine Learning;
- 20-25% on current experiments and training data models;
- 35 to 40% on the deployment and management of machine learning products; and
- 10% on implementing responsible machine learning practices.
Microsoft offers four free learning paths that together cover much of the exam material:
These courses vary in duration from three to 10 hours. For even more study material, Udemy hosts the Microsoft Azure Data Scientist DP-100 Comprehensive Exam Preparation Course.
The Azure certification exam has 40 to 60 questions, lasts 100 minutes, and costs $ 165.
Consider filling up Coursera Machine Learning Specialization Track. It has an organized list of four full courses to take. These courses deepen the important concepts and practices of machine learning. Only register for this track if you have previous machine learning experience.
Google Cloud Professional ML Engineer Certification
The Google Cloud Professional ML Engineer certification covers six main categories:
- frame ML problems
- creation of ML solutions
- design data preparation and processing systems
- develop ML models
- automate and orchestrate ML pipelines
- monitor, optimize and maintain ML solutions
Google recommends at least three years of hands-on experience with its cloud platform before taking the exam. But Google provides a recommended learning path for this certification to get familiar with machine learning with its cloud platform. from google Machine learning and artificial intelligence The path begins with the fundamentals of big data and machine learning. Then it progresses into topics like Google’s ML TensorFlow platform, MLOps automation frameworks, and ML pipelines.
The Google Cloud Professional ML Engineer certification exam can be taken remotely or at a local test center. It lasts two hours and costs $ 200.
The other preparation material includes a course, Google Cloud Certification Preparation: Professional Machine Learning Engineer’s Certificate, created by Google, which you can find on Coursera. Google Cloud Professional Data Engineer: Get Certified Training on Udemy Also teaches the machine learning and data pipelines topics covered by the exam.