Machine Learning Certification with AWS
Machine learning certification opens many exciting doors. Amazon Web Services (AWS) surveys of IT leaders found that 94% of them agreed that certified team members provide added value above and beyond the cost of certification. Some 73% of them are prioritizing job candidates with skills in artificial intelligence (AI). AWS has added three new certifications and resources to help you prepare for these exams. Given the high demand for these skills, there’s never been a better time to get certified.
The growth of artificial intelligence and specifically machine learning (ML) is revolutionizing nearly every field. It has the potential to reshape the world and every aspect of information technology. If you want to be part of this AI revolution, AWS machine learning certification makes sense to include in your education journey. You have a choice of AWS machine learning certifications, each with different exams.
The 3 Levels of AWS ML Certification
Amazon Web Services offers three levels of AI/machine learning certification. Which one is right for you depends on your knowledge and experience.
AWS Certified AI Practitioner
This is a basic certification that gives you a foundational understanding of core concepts in artificial intelligence and machine learning and how to use them in the AWS environment.
What You Need to Know Ahead of the Exam
Key knowledge areas covered by the exam include:
- AI concepts and terms with practical use cases
- Machine learning concepts, including the distinctions between supervised, unsupervised, and reinforcing learning
- How to use common ML algorithms
- What generative AI is
- Using generative AI with AWS services
- Core AWS AI and ML services and performance metrics
- Ethical considerations around AI and ML
Tasks You Won’t Be Expected to Perform
Since this is a basic certification, you won’t be diving into the technical details such as:
- Implementation through model development or writing code
- Designing algorithms and their mathematical intricacies
- In-depth AWS AI and ML services configuring
AWS Certified Machine Learning Engineer — Associate
This certification takes your AI and ML knowledge to the next level in developing, deploying, and managing applications using AWS services.
What You Need to Know Ahead of the Exam
The exam to earn this machine learning certification covers the following:
- Data engineering processes like ingesting, transforming, and getting data ready for ML models
- How to use AWS data processing services, including AWS Glue and Amazon S3
- Analysis and visualization of datasets
- Extracting data insights, identifying patterns, and finding anomalies
- Selecting algorithms appropriate to business problems
- How to train, tune, and evaluate ML models
- How to engineer and implement features
- Operationalizing ML models
- Maintaining and optimizing ML applications
- Monitoring performance metrics
- Using MLOps for continuous ML model integration and delivery
Tasks You Won’t Be Expected to Perform
You won’t be expected to design algorithms and understand the mathematical foundations of ML. Although it helps to have some knowledge of coding at a basic level, you won’t be doing in-depth programming on the test.
AWS Certified Machine Learning — Specialty
This certification is currently the highest level offered, intended for people with at least two years of experience working on ML apps on AWS. You’ll need to demonstrate your expertise in building, training, tuning, and deploying ML through AWS services.
What You Need to Know Ahead of the Exam
You will need to show proficiency in:
- Creating data repositories, including data warehouses and data lakes
- Implementing ways to ingest data into ML models
- Transforming data for ML
- Understanding data characteristics and visualizing data
- Framing business problems as ML tasks and training models
- Techniques for statistical analysis and data visualization
- Selecting and optimizing algorithms and performance metrics
- Deploying Amazon SageMaker and other related AWS services
- Continuous integration and delivery pipelines for ML workflows
Tasks You Won’t Be Expected to Perform
You won’t be tested on:
- Deployment automation
- Deep MLOps pipelines
- DevOps tools like Terraform or Kubernetes
- Writing ML models from scratch
Which AWS Certification Is Right for You?
A certification in machine learning opens doors and helps you stand out as a job candidate. Which is the best credential for your career goals? The AWS Certified AI Practitioner is for beginners with some familiarity with AI/ML tech and solutions on AWS; it will help you find entry-level jobs. If you have a year or so of AI or ML experience, and you seek a role as a software, MLOps, or data engineer, earning your AWS Certified Machine Learning Engineer – Associate is a great option. If you have two or more years and want to roll up your sleeves designing ML solutions or working as an AI data scientist, choose the AWS Certified Machine Learning – Specialty credential.
How to Prepare for a Career in Machine Learning?
Machine learning and AI roles are in high demand, making them an attractive career path in the field of computer science. A bachelor’s degree program in computer science, particularly one with a concentration in artificial intelligence, gives you not only the knowledge and skills you need to pass AWS machine learning certification exams but also a solid foundation for a variety of roles, setting you up for a career of steady advancement in the field.
Given the central role that artificial intelligence and machine learning will play in nearly all technology roles across industries, getting certified makes sense for your career. Each of the three levels of certification offered by AWS validate your skills and knowledge while helping you stand out in the job market. If you want to thrive in the AI-powered future, these certifications open a pathway.
