How did I clear my AWS ML Specialty Certification on the first attempt?

Some helpful advice and pointers to pass AWS Machine Learning-Specialty Exam

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If you are on this page, then you belong to any of these categories:

• Scheduled AWS ML Specialty exam and looking for the right resources & strategy to prepare for it
• Finding more about this exam, and design your strategy to prepare for it before scheduling

Doesn’t matter which category you belong to, this blog will guide you and give you some overview of one of the most challenging certification exams.

Recently I cleared my AWS Machine Learning Specialty Exam on the first attempt without having any prior AWS certification (which is not recommended) but had prior experience working on AWS. The exam was quite challenging but you can easily handle it if you have good experience in machine learning, and an understanding of different ML use cases and AWS services.

Let’s start with understanding more about this exam first from its official page.

What is the AWS Machine Learning Specialty exam?

The AWS Certified Machine Learning — Specialty (MLS-C01) exam is a certification exam for professionals who want to demonstrate their expertise in designing and implementing machine learning (ML) solutions on the Amazon Web Services (AWS) platform. The exam tests the candidate’s ability to use ML tools, techniques, and frameworks to build and deploy ML models on AWS. The exam is designed for professionals who have a strong understanding of ML concepts and hands-on experience using AWS services to design and implement ML solutions.

To earn the AWS Certified Machine Learning — Specialty certification, one must pass the MLS-C01 exam. The exam consists of 65 multiple-choice and multiple response questions, and candidates have 180 minutes to complete it. Results for the exam are reported as a scaled score of 100–1000. The minimum passing score is 750. The exam covers a wide range of topics, including:

• Understanding the AWS ML ecosystem
• Preparing and processing data for ML
• Choosing the appropriate ML algorithm and technique
• Training and deploying ML models
• Evaluating and optimizing ML models
• Managing ML workflows

To prepare for the AWS Certified Machine Learning — Specialty exam, one should have a strong foundation in ML concepts and techniques, as well as hands-on experience using AWS services for ML. It is recommended that candidates have at least two years of experience working with ML, including experience designing, training, and deploying ML models on AWS.

Why take the AWS Machine Learning Specialty exam?

There are several reasons why we might choose to take the AWS Certified Machine Learning — Specialty (MLS-C01) exam:

1. To demonstrate expertise in designing and implementing machine learning (ML) solutions on AWS: The AWS Certified Machine Learning — Specialty certification is a recognized credential that demonstrates a professional’s expertise in designing and implementing ML solutions on AWS. This can be valuable for professionals who want to show their skills and knowledge in this area to potential employers or clients.
2. To advance their career: Earning the AWS Certified Machine Learning — Specialty certification can help professionals advance their careers by demonstrating their skills and knowledge in a high-demand area. This can lead to new job opportunities or promotions in organizations that use AWS for ML.
3. To stay up-to-date with the latest ML technologies and best practices: The AWS Certified Machine Learning — Specialty exam covers a wide range of topics related to ML on AWS, including preparing and processing data, choosing the appropriate ML algorithms and techniques, and managing ML workflows. By taking the exam, individuals can stay up-to-date with the latest ML technologies and best practices and improve their skills in this area.
4. To gain a competitive edge: In a competitive job market, earning a certification can give professionals a competitive edge by demonstrating their commitment to their field and their expertise in a specific area. The AWS Certified Machine Learning — Specialty certification is a valuable credential that can help professionals stand out from other candidates and increase their visibility in the job market.

What are the prerequisites for the AWS ML Specialty exam?

There are no official prerequisites for the AWS Certified Machine Learning — Specialty (MLS-C01) exam. However, it is recommended to have at least two years of experience working with machine learning (ML), including experience designing, training, and deploying ML models on AWS.

To prepare for the exam, have a strong foundation in ML concepts and techniques, as well as hands-on experience using AWS services for ML. It is also recommended to have a solid understanding of core AWS services, such as Amazon S3, Amazon EC2, and Amazon EBS, as well as experience using AWS services for data storage, processing, and analysis, such as Amazon Redshift, Amazon Athena, and Amazon EMR.

In addition to hands-on experience, also be familiar with the exam topics covered on the AWS Certified Machine Learning — Specialty exam. The exam covers a wide range of topics, including understanding the AWS ML ecosystem, preparing and processing data for ML, choosing the appropriate ML algorithm and technique, training and deploying ML models, evaluating and optimizing ML models, and managing ML workflows.

Few experts/certified professionals do recommend having certain AWS certifications before attempting this one like AWS Certified Cloud Practitioner/Solutions Architect/Data Analytics Specialty/Security Specialty, but you can skip these and directly prepare for AWS ML Specialty.

How to prepare for AWS Machine Learning Specialty Exam?

To prepare for the AWS Machine Learning Specialty Exam, we should have a strong understanding of the following topics:

1. Machine Learning Concepts and Techniques: This includes understanding the different types of machine learning algorithms, the types of problems they are best suited for, and how to evaluate their performance.
2. AWS Machine Learning Services: We should be familiar with the different machine learning services offered by AWS, including Amazon SageMaker, Amazon EMR, Amazon EC2, Amazon ECS, Amazon EKS, and AWS Deep Learning AMIs, and how to use them to build, train, and deploy machine learning models.
3. Data Preparation and Exploration: We should be able to prepare and explore data for machine learning, including understanding how to clean and prepare data, select features, and perform exploratory data analysis.
4. Model Training and Evaluation: We should be able to train and evaluate machine learning models using different algorithms and techniques, including using hyperparameter tuning and cross-validation to optimize model performance.
5. Deploying and Managing Machine Learning Models: We should be able to deploy machine learning models to production and manage them in a live environment, including monitoring model performance and making updates as needed.

To prepare for the exam:

• Review the AWS Machine Learning Specialty Exam Guide, which outlines the specific topics that will be covered in the exam
• Study the AWS documentation and take online courses or attend training sessions to gain a deeper understanding of the material
• Practice building and deploying machine learning models using the AWS services in a hands-on setting to gain practical experience
• Review the AWS Machine Learning Competency page, which provides a list of recommended resources for preparing for the exam
• Appear for mocks before the actual attempt

List of Resources to Prepare for the AWS ML Specialty Exam

There are several resources available to prepare for the AWS Certified Machine Learning — Specialty (MLS-C01) exam:

1. AWS Machine Learning Competency page: This page provides a list of recommended resources for preparing for the AWS Certified Machine Learning — Specialty exam, including training courses, technical documentation, and white papers.
2. AWS Certified Machine Learning — Specialty Exam Guide: This guide outlines the exam objectives and provides a list of the topics that will be covered on the AWS Certified Machine Learning — Specialty exam.
3. AWS Machine Learning Blog: The AWS Machine Learning blog provides updates and information on the latest ML technologies and best practices on AWS.
4. AWS Machine Learning documentation: The AWS Machine Learning documentation provides detailed information on the various ML services and tools available on the AWS platform, including Amazon SageMaker, Amazon EMR, and Amazon Redshift.
5. AWS Machine Learning sample notebooks: AWS provides a collection of sample notebooks that demonstrate how to use various ML tools and techniques on the AWS platform.
6. AWS Machine Learning webinars and events: AWS regularly hosts webinars and events on ML topics, which can be a valuable resource for candidates preparing for the AWS Certified Machine Learning — Specialty exam.
7. Online ML courses: There are a variety of online courses and training programs available that cover ML concepts and techniques, including courses specifically designed to prepare individuals for the AWS Certified Machine Learning — Specialty exam. I referred AWS Machine Learning — Specialty Certification Preparation learning path on Cloud Academy and practiced by myself for building end-to-end ML pipelines.
   There are other online courses as well which you can refer to, a few of them are listed below:
   - AWS Certified Machine Learning Specialty 2022 — Hands-On
   - AWS Certified Machine Learning — Specialty (MLS-C01)
   - Amazon SageMaker Technical Deep Dive Series
   - AWS Certified Machine Learning — Speciality

I would recommend to must review the AWS Certified Machine Learning — Specialty Exam Guide and the AWS Machine Learning Competency page to get a thorough understanding of the exam objectives and the recommended resources for preparing for the exam. In addition to these resources, I also gained hands-on experience working with ML on the AWS platform to gain a practical understanding of the concepts and techniques which helped in the exam.

That’s all about my experience with the AWS Machine Learning Speciality exam. The only thing you need to do now is pick the right resources, prepare your plan and start learning.

If you find this helpful, feel free to share it. You can also drop a comment or ping me on LinkedIn if you have any doubts or questions.

Happy learning and Best of luck with the exam!