Theory | Hands-On Labs | Full Practice Exam with Explanations | Downloadable PDF Slides | Pass the certification exam
β±οΈ Length: 54.7 total hours
β 4.28/5 rating
π₯ 12,409 students
π February 2026 update
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- Course Overview
- This comprehensive and highly-rated program, “AWS Certified ML Engineer Associate – Theory, Hands-On, Exams,” is meticulously designed to prepare you for the challenging AWS Certified Machine Learning β Specialty examination. It offers an unparalleled blend of theoretical depth, extensive hands-on labs, and dedicated exam preparation, ensuring mastery in deploying robust ML solutions on Amazon Web Services.
- Encompassing 54.7 total hours of rich content, this course guides learners through a structured curriculum, evolving from foundational ML principles to advanced MLOps practices within the AWS cloud. The emphasis is on practical application, bridging the gap between theory and real-world implementation.
- With an exceptional 4.28/5 rating from over 12,409 students, this course stands as a testament to its quality and effectiveness. The high student satisfaction underscores its success in delivering valuable, actionable knowledge and exam readiness.
- Regularly updated, with the latest significant revision in February 2026, the content ensures you learn the most current AWS services, best practices, and align perfectly with the latest certification exam objectives, future-proofing your skills in a dynamic tech environment.
- The learning experience includes numerous hands-on labs to solidify practical skills and a full practice exam with detailed explanations, meticulously crafted to simulate the actual certification environment and enhance your readiness.
- Requirements / Prerequisites
- A solid working knowledge of Python programming is essential, as it’s heavily used for AWS service interaction and ML model development in labs.
- Familiarity with fundamental machine learning concepts, including supervised/unsupervised learning, common algorithms, and basic evaluation metrics, is highly recommended.
- Basic exposure to core AWS cloud services such as S3, EC2, and IAM will provide a beneficial foundation.
- An analytical mindset and strong problem-solving abilities are advantageous for navigating complex ML challenges.
- Skills Covered / Tools Used
- Amazon SageMaker Expertise: Master the full ML lifecycle on SageMaker, covering data processing (SageMaker Processing), feature engineering, model training (built-in algorithms, custom frameworks like PyTorch/TensorFlow), hyperparameter tuning, robust model deployment (real-time endpoints, batch transform), and comprehensive model monitoring (SageMaker Model Monitor).
- AWS AI/ML Service Integration: Learn to effectively utilize and integrate specialized AWS AI services, including Amazon Rekognition, Comprehend, Textract, Translate, Transcribe, and potentially Amazon Forecast/Personalize, to build intelligent applications.
- ML Data Management & Engineering: Develop skills in preparing and managing datasets using Amazon S3, integrating with databases (RDS, DynamoDB), and performing ETL operations with AWS Glue for scalable ML workflows.
- Advanced MLOps Practices: Implement end-to-end Machine Learning Operations (MLOps) on AWS, focusing on CI/CD for ML, orchestrating pipelines with SageMaker Pipelines, model versioning, and continuous monitoring for performance and drift.
- Security & Cost Optimization for ML: Secure ML workloads with AWS IAM, VPC, and KMS, and learn strategies for optimizing costs associated with ML training and inference infrastructure.
- Python & AWS SDKs: Gain extensive hands-on experience scripting and automating ML workflows using Python, the AWS SDK (Boto3), and the SageMaker Python SDK within Jupyter notebooks.
- Benefits / Outcomes
- Confidently pass the AWS Certified Machine Learning β Specialty exam, earning a highly respected, industry-recognized certification.
- Acquire the practical expertise to architect, build, train, deploy, and maintain production-grade machine learning solutions and MLOps pipelines on AWS.
- Significantly advance your career in roles such as ML Engineer, Data Scientist, or MLOps Specialist.
- Develop a deep understanding of best practices for model governance, security, and cost efficiency within AWS ML environments.
- Build a strong portfolio of hands-on projects, showcasing your ability to solve real-world business problems using AWS Machine Learning.
- PROS
- Comprehensive & Practical: Exceptional blend of theory, extensive hands-on labs, and targeted exam preparation.
- High Student Satisfaction: Endorsed by excellent ratings from over 12,000 students, highlighting proven effectiveness.
- Up-to-Date Content: Regularly refreshed (February 2026 update) ensuring relevance with the latest AWS services and exam objectives.
- Certification Focused: Explicitly designed to maximize your success in the AWS Certified Machine Learning β Specialty exam.
- Downloadable Resources: Convenient access to PDF slides and other materials for flexible offline study and reference.
- CONS
- Significant Time Commitment: The extensive 54.7 total hours demand substantial dedication and consistent effort for effective completion.
Learning Tracks: English,IT & Software,IT Certifications
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