Are you preparing to take your machine learning certification exam?
Two popular options in the industry for ML certifications are AWS Machine Learning Specialty vs Google ML Engineer Certification. Both certifications provide a clear learning path and improve your ML skills, but they are for different needs and backgrounds
Choosing the right ML certification for your career upskilling can help you advance in your field.
This AWS Certified Machine Learning Specialty Certification certification is for those who want to create and deploy machine learning models using AWS services and this certification will test your ability to use AWS tools like SageMaker and Rekognition. It is ideal if you work with the AWS platform or plan to use it for your projects.
The Google Cloud Professional Machine Learning Engineer Certification focuses on designing, creating, and deploying machine learning models on Google Cloud. It covers machine learning models, data pipelines, and optimizing ML solutions.
In this blog, we will delve into the differences between two such important certifications as AWS Machine Learning Specialty Certification and Google Professional Machine Learning Engineer Certification, and their focus, eligibility & salary prospects.
AWS Machine Learning Specialty vs Google ML Engineer Overview
About AWS Certified Machine Learning Specialty Certification
The AWS machine learning certification is for individuals who are in a development or data science role. It validates deep knowledge of building, training, tuning, and deploying machine learning models using the AWS cloud and recommends implementation of best practices throughout the ML lifecycle.
It validates advanced technical skills in ML and covers domains such as:
- Data Engineering
- Exploratory data analysis
- Modeling
- Machine learning implementation and operations
Key Skills Acquired in AWS Machine Learning Specialty Certification
With this AWS Machine Learning Specialty Certification (MLS-C01) you will gain several practical skills that are necessary for implementing machine learning solutions:
- Data Preparation & Model Building: Learn data cleaning, analysis, and manipulation. And to build the machine learning models using Amazon SageMaker
- Deployment and Monitoring: Learn how to deploy models, create inference endpoints, and monitor your model’s performance with AWS tools.
- Security and Compliance: Understand how to manage data securely and adhere to relevant regulations when working on machine learning projects.
About Google Professional Machine Learning Engineer Certification
The Google Professional Machine Learning Certification is for professionals who use Google Cloud to create and develop machine learning models. It emphasizes responsible AI practices and the use of Google Cloud’s ML tools and infrastructure.
This GCP Machine learning certification proves your skills in:
- Defining ML problems
- Designing ML solutions
- Automating and managing ML pipelines
- Monitoring and improving ML models
Key Skills Acquired in Google Professional Machine Learning Engineer Certification
The Google Professional Machine Learning Engineer Certification is designed to give you a deep understanding of machine learning systems skills on:
- Machine Learning Concepts: Gain a solid foundation in machine learning theory, including supervised and unsupervised learning techniques.
- Model Development and Evaluation: To create, develop, and assess machine learning models, it guides how to use programs like TensorFlow and Vertex AI.
- Deployment Strategies: Learn the best ways to use Google Cloud to deploy MLOps models at a large scale.
- Data Pipeline Optimization: Concentrate on developing efficient data pipelines to enable diverse machine learning techniques.
Factors to Consider in Choosing AWS ML and Google ML Certification
What types of positions are you looking for in your career goals?
Sure if you like to work mainly on AWS technologies then the best is only to go for AWS certification. On the flip side, if you are also interested in Google Cloud, Professional Machine Learning Engineer certification would be a better fit.
AWS Machine Learning Specialty and Google ML Engineer: Roles & Responsibilities
AWS Machine Learning Specialty Roles and Responsibilities
As an AWS Machine Learning Specialty professional, you use AWS services to build, train, and deploy machine learning models. Your main focus is on optimizing and securing ML solutions within the AWS ecosystem.
As an AWS machine learning certified, their primary responsibilities include:
- Design and build scalable ML models using AWS services such as SageMaker, ASW Lambda, and EC2.
- Train and tune models using AWS ML tools to improve performance and accuracy.
- Deploy ML models securely and at scale using AWS Deployment Services. of.
- Using AWS Glue and AWS Data Pipeline, creating data preprocessing and feature engineering pipelines
- Monitoring and troubleshooting ML models to ensure they perform well and reliably.
- Ensuring the security and compliance of ML models and data with AWS IAM and KMS.
- Working with data scientists and engineers to integrate ML models into applications and workflows.
- Evaluation and validation of models to meet business and technical requirements.
- Optimizing resource utilization and cost efficiency for ML workloads in AWS.
Google Professional Machine Learning Engineer Roles and Responsibilities
As a Google Professional Machine Learning Engineer, you will design, build, and manage ML models using Google Cloud tools. This role focuses on MLOps and responsible AI practices.
As a GCP Machine learning certified, your primary responsibilities include:
- Designing and developing ML models with Google Cloud’s Vertex AI, TensorFlow, and other services.
- Creating MLOps pipelines to automate the deployment and monitoring of ML models using tools like Cloud Build and Cloud Functions.
- Build and manage data processing pipelines using Google Cloud Dataflow, BigQuery, and Cloud Storage.
- Continuous monitoring and optimization of model performance and reliability.
- Applying Responsible AI principles to ensure fairness, transparency, and accountability in ML models.
- To work with cross-functional teams to integrate ML solutions into business processes and applications.
- Validating and testing models to ensure they meet performance and accuracy standards.
- Optimizing the use of Google Cloud resources for cost-efficiency and scalability.
- Ensuring the security and compliance of ML models and data, following Google Cloud’s best practices.
Difference of Job and Salary Outlook: AWS Machine Learning Specialty and Google ML Engineer
Job outlook & salary-wise, both certifications provide a great scope in the future.
AWS Machine Learning Specialty Certification: Job Outlook
As a result of the momentum behind cloud-based ML solutions, there is now an increasing demand for professionals with AWS machine learning experience. It is a widely respected certification that provides access to many professionals.
Having this AWS ML certification helps professionals opt for the following roles:
- AWS Machine Learning Engineer: Develop and Deploy ML models in AWS Services.
- Data Scientist: Analyzes complex data sets to derive insights and build predictive models.
- Data Scientist: Processes large data sets to find patterns and build predictive models.
- AI Specialist: Designs, develops, and deploys AI models on AWS.
- ML Ops Engineer: Operationalize ML models (make them scalable and reliable)
AWS Machine Learning Specialty Certification: Salary Outlook
Given that, you know the AWS Machine Learning Specialty salaries, and with other in-demand, highly specialized skill sets being introduced every month due to cloud technologies and architectures advancing at a rapid pace this could certainly change.
As Per an industry survey, typical salaries for these roles are:
- Machine Learning Engineer: $110,000 to $150,000 per year
- Data Scientist: $100,000 to $140,000 per year
- AI Specialist: $105,000 to $145,000 per year
- ML Operations Engineer: $115,000 to $155,000 per year
Google Professional Machine Learning Engineer: Job Outlook
As AI and machine learning grow rapidly, so there’s an urgent need for Google Cloud ML tools and infrastructure professionals. Google’s Professional Machine Learning Engineer certification is highly regarded and prepares professionals for a variety of roles.
Google Certified Professional Machine Learning Engineers can work in roles such as:
- Machine Learning Engineer: Designs, builds, and maintains ML models in Google Cloud.
- AI Engineer: Develops applications and systems based on artificial intelligence using Google tools.
- Data Engineer: Manages and optimizes data pipelines and storage for ML applications.
- ML Ops Engineer: Ensures smooth implementation and operation of ML models with a focus on MLOps practices.
Google Professional Machine Learning Engineer: Salary Outlook
Salaries for Google Professional Machine Learning Engineers are attractive, reflecting the high demand for their expertise. The median salaries for these positions are:
- Machine Learning Engineer: $115,000 to $160,000 per year
- AI Engineer: $110,000 to $150,000 per year
- Data Engineer: $105,000 to $140,000 per year
Choosing the right certification:
But which Machine Learning certification is right for you will largely depend on your career goals, and of course how adept they are with AWS / Google Cloud. Take into account your level of excitement with waking up to a new platform every day. In the end, you can’t make a bad decision either way; whichever certification aligns best with your future personal and professional goals is probably going to be for the better.
Content and Focus:
While both certifications cover similar foundational topics in machine learning, their focus areas differ significantly.
AWS Machine Learning Specialty Certification Focus:
- This AWS ML certification focuses on the usage of AWS services such as SageMaker, AWS Lambda, and AWS EC2.
- It also has a strong focus on data engineering and how the machine learning models work.
Google Professional Machine Learning Certification Focus:
- It focuses on Google Cloud services such as BigQuery, TensorFlow, and the AI platform.
- More emphasis on the complete machine learning lifecycle, including model evaluation and monitoring.
Prerequisites and Experiences:
Both ML certifications require a deep understanding of machine learning concepts, but they have different prerequisites.
AWS Machine Learning Specialty Certification:
- At least one or two years of hands-on data science or machine learning experience is recommended.
- Knowledge of AWS services is helpful but not required.
Google Professional Machine Learning Certification:
- It requires strong machine learning and experience using Google Cloud.
- We recommend experience designing and building ML models.
Latest Updates:
Recent Updates in AWS Machine Learning Specialty Certification
In 2024, the AWS Certified Machine Learning Specialty Certification has several significant updates to keep up with the evolving landscape of cloud computing and machine learning.
Exam Content and Structure: The certification exam (MLS-C01) focuses on evaluating a candidate’s ability to create, train, configure, and deploy machine learning models in AWS. It contains a combination of multiple-choice and multiple-choice questions. The exam takes 180 minutes and costs $300. It is available in languages such as English, Japanese, Korean, and Simplified Chinese.
Training and Preparation: AWS has introduced new digital training courses and updated existing ones to help candidates prepare for this MLS-C01 certification. These courses cover topics such as machine learning exam basics, the CRISP-DM process model, elements of data science, and machine learning security. AWS Skill Builder program now offers both free and subscription-based resources such as hands-on labs and game-based learning.
Roles and domain-specific training: AWS brings role-based training for cloud professionals, architects, and developers, as well as domain-specific training for advanced web and machine learning. This includes practical applications and the use of AWS services such as Amazon SageMaker to build and deploy models.
Recent Updates in Google Professional Machine Learning Engineer
In the first half of 2024, Google made several important updates to the Professional Machine Learning Engineer certification:
Updated Exam Content: The exam content has been revised to emphasize more advanced skills and current industry practices. This includes an increased focus on formulating ML problems, designing ML solutions, and developing and deploying ML models using Google’s cloud technologies.
New Study Paths: Google has introduced updated study paths and study materials to help candidates better prepare for the GCP Machine Learning exams. These resources are designed to provide a more structured and effective learning experience
FAQ
1. How much does each AWS & Google Cloud Machine learning certification cost?
This AWS Machine Learning Specialty Certification costs around 300 USD, while the Google Professional Machine Learning Engineer certification costs 200 USD.
2. Which certification is better known in the industry?
Both certifications are highly recognized, but the AWS certification is often preferred in industries that use a lot of AWS infrastructure, while the Google certification is preferred in industries that use Google Cloud.
3. What are the prerequisites for these certifications?
AWS recommends 1-2 years of ML experience at AWS, while Google recommends 3+ years of experience in this field and 1+ years of designing and managing Google Cloud solutions.
Conclusion
Hopefully, this blog post will help you compare and choose between AWS Machine Learning Specialty and Google ML Engineer certifications.
Ultimately, finding the right one depends on your career goals, the technologies you want to work with, and the industries you’re targeting. Both certifications provide valuable knowledge and skills that can advance your career in machine learning.
AWS specializes in the practical aspects of machine learning based on AWS cloud services, while Google wanted to focus very strongly not only on application development but also on building the entire model and deployment strategy.
Experience hands-on learning of these AI and machine learning concepts through our guided AWS and Google Cloud hands-on labs and also experiment with your learning through our AWS Sandbox and Google Cloud Sandbox.
Happy learning!
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