Mapping your AI/ML career journey

In today’s fast-paced world, businesses are turning to artificial intelligence (AI) and machine learning (ML) to stay ahead of the competition. If you’re thinking about a career in this field, Amazon Web Services (AWS) has a clear certification path that can help you grow your skills, starting from the basics and building up to advanced AI/ML expertise. I intend to walk you through AWS’s AI/ML certification path—from the AWS Certified AI Practitioner to the AWS Certified Machine Learning Engineer – Associate certification to the AWS Certified Machine Learning – Specialty—and share some resources that will help you in your journey.

Step 1: AWS Certified AI Practitioner (CLF-C02)

If you’re new to AI, the AWS Certified AI Practitioner (AIF-C01) certification is the perfect starting point. This certification emphasizes practical knowledge over technical depth, making it ideal for business professionals, decision-makers or anyone else curious about how AI can improve efficiency. The exam covers foundational AI/ML concepts, architectural patterns, ethical AI practices, and introduces you to core AWS tools like Amazon SageMaker, Amazon Comprehend, and Amazon Lex.

To prepare for this certification, AWS Educate and AWS Skill Builder provide structured learning paths. These resources offer interactive tools like AWS Escape Room: Exam Prep for AWS Certified AI Practitioner (AIF-C01), which is a unique way to learn by turning it into a gamified challenge. As a bonus, you can earn digital badges along the way, which are great to showcase on your resume.

However, it’s not just about theory—AWS also offers hands-on labs through AWS Educate and AWS Skill Builder. These labs let you dive into AI services in a real AWS environment, giving you the experience you need to bridge the gap between understanding concepts and applying them in the real world. This hands-on experience is invaluable for building your confidence with the tools you’ll be using on the job.

Step 2: AWS Certified Machine Learning Engineer – Associate (MLA-C01)

Once you’ve got the basics down, it’s time to take your skills to the next level with the AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification. This certification focuses on the complete ML lifecycle and is designed for practitioners who implement, deploy, and maintain ML solutions on AWS. It covers everything from data preparation and model training to workflow orchestration and monitoring.

AWS Skill Builder has you covered with an Exam Prep Plan that includes hands-on labs and games like AWS Cloud Quest and AWS Builder Labs, which make learning interactive and engaging. AWS also has a library of whitepapers and case studies to help you connect what you’re learning to real-world applications, and can see how different industries are using machine learning to solve problems.

One notable way to learn about ML is through AWS DeepRacer. With DeepRacer, you can program a 1/18th scale race car to drive itself around a track. It’s a hands-on and interactive way to learn about reinforcement learning, and participating in DeepRacer competitions is a great way to build both your technical skills and your problem-solving abilities—two key traits for any ML professional.

Step 3: AWS Certified Machine Learning – Specialty (MLS-C01)

If you want to take your skills even further, the AWS Certified Machine Learning – Specialty (MLS-C01) certification is the next logical step. This certification is aimed at professionals who have at least two years of experience working with ML in AWS. It dives deep into advanced topics like data engineering, analytics, and optimizing models. With this certification, you’ll be ready for roles like Machine Learning Engineer, Data Scientist, or AI Architect.

This certification validates that you can design and implement sophisticated ML solutions. You’ll need to know how to clean and transform data for analysis and be comfortable using tools like Amazon Kinesis for real-time data processing and Amazon SageMaker for building and deploying models. These skills are increasingly valued, especially as organizations work to implement large-scale AI solutions to stay competitive.

To help you prepare, classroom courses like Practical Data Science with Amazon SageMaker and Machine Learning Pipeline on AWS offer plenty of hands-on experiences. These courses will walk you through building and optimizing ML models, as well as implementing responsible AI practices. AWS also provides practice exams and additional resources to help you tackle key topics like bias detection, data privacy, and fairness—important considerations for building trustworthy AI systems.

Optional certifications to boost your career

Beyond the core AI/ML certifications, you might also want to consider AWS Certified Solutions Architect – Professional (SAP-C02) or AWS Certified Data Engineer – Associate (DEA-C01) to round out your skills.

The AWS Certified Solutions Architect – Professional certification shows that you can design complex AWS solutions and optimize cloud performance—ideal for those wanting to provide strategic guidance across multiple projects.

The AWS Certified Data Engineer – Associate certification focuses on managing data pipelines, optimizing performance, and working with services like Amazon Redshift and AWS Glue. Data engineering is a crucial part of any AI/ML project—having a certification in this area means you’re well-equipped to prepare and manage the data that’s needed to train effective ML models.

What’s new: exam question types and enhanced learning

AWS keeps its certification exams up to date with industry trends, which means you’ll find some new question types, such as ordering, matching, and case study questions in the AWS Certified AI Practitioner and AWS Certified Machine Learning Engineer – Associate exams. These new formats are all about practical thinking and applying what you know rather than just memorizing facts.

Case study questions, in particular, present real-world challenges and require you to use your knowledge to solve problems. This is a great way to ensure that you’re developing practical skills, not just learning theory. These types of questions help you build critical thinking and problem-solving skills, which are exactly what you’ll need when faced with the unpredictable challenges that come up in machine learning projects.

A practical learning journey

The journey from AWS Certified AI Practitioner to AWS Certified Machine Learning – Specialty offers a structured approach that helps you grow—from understanding fundamental AI concepts to handling complex ML projects in a cloud environment. By using resources like AWS Skill Builder, AWS Educate and the Udemy Business Leadership Academy cohort programs, you can accelerate your learning and stay ahead of the competition in the fast-moving AI/ML landscape.

The AWS Certification journey isn’t just about passing exams; it’s about building practical skills that will set you apart in the workplace. AI and ML are rapidly changing fields, and staying current is the key to your success. By taking advantage of AWS’s hands-on labs and practical challenges, you’ll be ready not only for exams but for applying your skills in real-world scenarios where you can truly make an impact.

Whether you’re looking to pivot into a tech career, expand your expertise, or bring AI/ML capabilities into your organization, AWS offers a clear path to help you succeed. Start with the AWS Certified AI Practitioner and work your way up. By following this approach, you’ll be well-equipped to make meaningful contributions in AI and ML and drive innovation in your industry.

Additional resources

• Get on the path to the AWS Certified AI Practitioner with AWS Educate
• AWS Certification: Addition of new exam question types
• Inside look at the new AI and ML AWS Certifications