Why AWS Is Retiring the Machine Learning Specialty Cert

The AWS Machine Learning – Specialty certification will cease to be offered on March 31, 2026, according to Amazon Web Services (AWS). Meanwhile, AWS is introducing the AWS Certified Generative AI Developer – Professional cert for more production-focused AI skills.

If this update affects your learning path, here are the details you need to know.

Why AWS is Moving Away From a Model-First Certification

When the AWS Machine Learning – Specialty exam was introduced, most organizations were still experimenting with building and tuning models from scratch. That approach required deep knowledge of algorithms, training pipelines, and feature engineering.

Today, that’s no longer how most teams work.

Instead of training custom models, organizations increasingly rely on managed foundation models and Amazon Web Services cloud services that integrate directly into applications. Tools like Amazon Bedrock allow developers to deploy generative AI features without owning the entire model lifecycle, while Amazon SageMaker continues to support training and deployment when customization is needed.

As a result, AWS certifications are shifting toward validating whether professionals can operate AI in production by managing identity, cost controls, security boundaries, observability, and integration with other cloud services. Those skills fall outside the original scope of the ML Specialty exam, which focused more heavily on model development theory.

To expand your AWS background, CBT Nuggets offers the AWS Machine Learning Engineer Associate course. Work with real datasets, train models, and push them into live AWS systems using tools such as SageMaker.

How Generative AI Has Changed Skill Needs

Here are the skills you’ll need to have as a generative AI developer:

• Deploy AI services inside web applications and the cloud

• Manage user access and service costs

• Set up AI tools to pull data through APIs and pipelines

• Secure cloud and AI resources

• Observe model behavior in live systems

Should You Finish the ML Specialty or Switch?

If you're studying now, you have choices until March. Here's how to decide: 

• Finish the ML Specialty if: You’re close to exam-ready and your role focuses on data science, experimentation, or model evaluation.

• Switch if: Your job involves deploying AI features, managing cloud services, or supporting AI-enabled applications, or you won't be ready to test before the cert is retired. 

What This Means for Current ML Specialty Candidates

If you already earned the ML Specialty certification, it will remain visible on your AWS transcript for three years. But if you’re in the middle of training, you need to factor in the retirement date and determine whether to finish or switch to a new exam.

Many IT teams now consider cloud setup, automation, and AI as one single track. See if older ML tracks are still relevant compared to new certifications. Determine whether they still match your current role or the ones you’re interested in. You can also plan your training around tools like Bedrock and SageMaker.

Browse AWS courses available at CBT Nuggets, including the Machine Learning Engineer Associate course for hands-on practice. Your first seven days of training are free, so sign up and take that next step today.