The AWS Machine Learning Specialty certification is a valuable credential for coders, developers, AI enthusiasts and others looking to demonstrate and validate their machine learning skills on the AWS platform. As machine learning continues transforming industries and job roles, this certification shows you have the expertise to properly implement ML solutions on AWS.
Get the official exam guide here: AWS Certified Machine Learning – Specialty (MLS-C01) Exam Guide
Offered by Amazon Web Services (AWS), the AWS Machine Learning Specialty certification tests your knowledge across a range of ML and deep learning topics. By passing this exam, you can highlight your ability to:
- Select and justify the right ML approach for a given business problem
- Identify the appropriate AWS services and tools to build ML solutions
- Design, implement, deploy and maintain scalable, cost-optimized and secure ML systems on AWS
If you’re interested in advancing your career and skills with machine learning and AWS, preparing for the AWS Machine Learning Specialty certification is a great next step. This article provides an overview of how to prepare, study and pass the exam.
Preparing for the AWS Machine Learning Specialty Certification
Recommended Skills and Experience
While AWS does not require candidates to take any specific training before the exam, they do recommend having 1-2 years of hands-on experience with machine learning and deep learning workloads on AWS.
Some key skills and knowledge areas that are helpful include:
- Understanding of ML algorithms and how to select the right approach
- Experience with ML/DL frameworks like TensorFlow, PyTorch, and SageMaker
- Ability to perform hyperparameter optimization and follow best practices for model training/deploying
- Knowledge of AWS services for data engineering, model building/hosting, and ML operations
Having experience developing or managing full lifecycle ML projects on AWS will best prepare you for the topics tested on the certification.
Exam Topics and Domains
The AWS Machine Learning Specialty certification exam covers a wide range of ML and AWS-related topics across four key domains:
- Data Engineering (20%): S3, Kinesis, Glue, Athena, Redshift, DynamoDB, Elasticsearch
- Exploratory Data Analysis (24%): Data exploration, time series, Athena, QuickSight, feature engineering
- Modeling (36%): ML algorithms, neural networks, AWS SageMaker, model training/tuning
- ML Implementation & Operations (20%): Deploying models, A/B testing, scaling, monitoring, security
Exam questions test your applied knowledge in selecting and using the appropriate AWS services to build, train, deploy and manage ML solutions. Be familiar with the major AWS offerings for each part of the ML workflow.
Study Resources
To prepare for the AWS Machine Learning Specialty certification, here are some recommended study resources:
- AWS Exam Readiness Training: Free digital courses to learn about AWS services and best practices for the exam. Includes sample questions.
- AWS Whitepapers: In-depth technical documents on using AWS services for ML. Great for hands-on learning.
- Online Courses: Self-paced or instructor-led courses from providers like A Cloud Guru, Cloud Academy, Linux Academy. Includes labs, assessments and videos.
- Books: AWS Certified Machine Learning Study Guide from Wiley. Official exam guide also available.
- Practice Tests: Simulate the exam experience with practice questions from sites like Whizlabs, KnowledgeHut, and others.
Combining reading, hands-on practice, video training and sample questions will fully prepare you for the topics tested on the certification exam.
Tips for Passing the Exam
Here are some top tips for successfully passing the AWS Machine Learning Specialty certification exam:
- Gain hands-on experience – Implement end-to-end ML projects on AWS to apply your knowledge.
- Take practice exams – Familiarize yourself with the format and question styles.
- Review key concepts and definitions – Know basics of ML algorithms, training processes, AWS services.
- Read documentation – Understand specifics of AWS services for ML.
- Focus studying on weaker domains – Check exam guide for percentage of questions per domain.
Supplement reading and courses with practical experience. Hands-on learning will boost exam performance and career skills.
Taking the Exam
Exam Format
The AWS Machine Learning Specialty exam contains 65 multiple choice and multiple response questions. You have 180 minutes (3 hours) to complete the exam.
It is a pass/fail exam scored against a minimum standard established by AWS. You will receive a scaled score between 100-1000. The minimum passing score is 750 (75%).
No partial credit is awarded for incomplete or incorrect answers. There are no prerequisites to take the exam, but AWS recommends completing the Cloud Practitioner certification first.
Registering for the Exam
You can register for the exam through your AWS Training account. The exam costs $300 USD, though discounted exam vouchers may be available.
The exam is available globally at test centers operated by PSI. You can take the exam on your own schedule after registering.
After Passing the Exam
Once you pass the exam and achieve the AWS Certified Machine Learning Specialty certification, you will receive an email confirmation and digital badge to showcase your accomplishment. The certification is valid for 2 years before needing renewal.
This credential can help open new job opportunities, earn higher pay, and demonstrate your cloud ML skills. However, the real value comes from the knowledge gained through preparation and hands-on experience.
To maintain the certification, you must recertify every two years. This involves passing the exam again or completing qualifying AWS coursework.
You can build on this certification by pursuing advanced AWS certifications to take your cloud and ML skills to the next level.
Is the AWS ML Certificate Worth it?
The AWS Machine Learning Specialty certification comes with a decent amount of effort and cost to attain, so is it worth pursuing? For those working in machine learning or cloud computing, the certification can definitely be worth the investment. Gaining this credential can help you differentiate your skills, gain expertise with AWS for ML, and potentially improve your career prospects and salary. Companies hiring for ML roles may specifically look for this certification when evaluating candidates. The preparation also allows you to deeply understand best practices for ML ops on AWS. While the certification requires dedicating time and money for study and exams, it showcases your specialized skillset on one of the most widely used cloud ML platforms. Overall, the AWS Machine Learning Specialty certification delivers excellent value for the right professionals looking to advance their ML and cloud skills.
Conclusion
The AWS Machine Learning Specialty certification validates your ability to create, deploy and manage machine learning solutions on AWS. Investing in this certification can enhance your career prospects and deepen your ML and cloud expertise.
Use the tips in this guide to effectively prepare through hands-on practice, study resources and sample questions. With diligent practice of key skills and concepts, you can succeed in passing the exam.
So start prepping today to earn this valuable certification and advance your machine learning career in the cloud!