Theory | Hands-On Labs | Practice Questions | Downloadable PDF Slides | Pass the certification exam | Latest Syllabus
β±οΈ Length: 25.8 total hours
β 4.50/5 rating
π₯ 7,348 students
π February 2026 update
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Course Overview
- Embark on a transformative journey designed to master machine learning on the AWS platform and achieve the highly sought-after AWS Certified Machine Learning Specialty certification. This comprehensive course, meticulously structured around the latest syllabus, offers a balanced curriculum encompassing foundational theory, extensive hands-on labs, and crucial practice questions to ensure your complete readiness.
- Spanning 25.8 total hours of in-depth instruction, this program is engineered for aspiring and experienced ML engineers, data scientists, cloud architects, and developers eager to validate their expertise in building, training, tuning, and deploying machine learning models on AWS.
- Benefit from a proven learning experience, reflected by an impressive 4.50/5 rating from over 7,300 satisfied students. With its content regularly updated, including a significant refresh in February 2026, you are guaranteed access to the most current AWS services and best practices in machine learning. Prepare to not just pass the exam, but to confidently apply advanced ML solutions in real-world scenarios.
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Requirements / Prerequisites
- A foundational understanding of machine learning concepts (e.g., supervised vs. unsupervised learning, basic algorithms, model evaluation metrics).
- Proficiency in Python programming, including familiarity with common data manipulation libraries like Pandas and NumPy.
- Basic working knowledge of the AWS cloud platform (e.g., navigating the console, understanding S3 buckets, IAM roles, EC2 instances).
- While not strictly mandatory, prior exposure to data science workflows would be beneficial.
- An active AWS account for hands-on lab exercises (please note: usage costs for AWS services may apply).
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Skills Covered / Tools Used
- Data Engineering for ML: Efficiently ingesting, transforming, and storing machine learning data using core AWS services like Amazon S3, Kinesis, AWS Glue, Amazon Athena, and Amazon Redshift; understanding feature stores and robust data governance strategies.
- Exploratory Data Analysis & Feature Engineering: Leveraging SageMaker Data Wrangler for visual data preparation and transformation; applying advanced techniques for handling missing values, encoding categorical data, scaling numerical features, and creating new, impactful features to enhance model performance.
- Model Training & Tuning: Deep dive into Amazon SageMaker for building, training, and deploying ML models at scale; utilizing SageMaker built-in algorithms (e.g., XGBoost, Linear Learner, BlazingText, Image Classification) and custom training with frameworks like TensorFlow and PyTorch; mastering hyperparameter tuning with SageMaker Automatic Model Tuning and optimizing distributed training strategies.
- Model Evaluation & Explainability: Thorough techniques for evaluating model performance using appropriate metrics (accuracy, precision, recall, F1-score, RMSE, R2); interpreting model predictions with SageMaker Clarify for bias detection and explainability (SHAP, LIME); monitoring model drift and data quality via SageMaker Model Monitor.
- Model Deployment & MLOps: Deploying models as real-time endpoints or batch transformations using SageMaker Hosting services; implementing robust CI/CD pipelines for machine learning models with SageMaker Pipelines and AWS CodePipeline; effective versioning of models and managing model registries; strategies for A/B testing and canary deployments to ensure seamless updates.
- Specialized AI Services: Integrating and leveraging pre-trained AI services: Amazon Rekognition (computer vision), Amazon Comprehend (natural language processing), Amazon Textract (document analysis), Amazon Transcribe (speech-to-text), Amazon Polly (text-to-speech), Amazon Translate (language translation), and Amazon Lex (conversational AI); utilizing higher-level ML services like Amazon Personalize (recommendation engines) and Amazon Forecast (time-series forecasting).
- Security & Cost Optimization: Implementing security best practices for ML workloads on AWS using IAM, VPC, and KMS; developing strategies for effective cost management and optimization of SageMaker and other ML resources.
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Benefits / Outcomes
- Successfully prepare for and pass the AWS Certified Machine Learning Specialty exam, validating your expertise in designing, implementing, and maintaining ML solutions on AWS.
- Gain extensive hands-on experience in building, training, deploying, and managing end-to-end machine learning workflows directly on the AWS platform.
- Develop a deep understanding of MLOps best practices, ensuring scalable, reliable, secure, and maintainable machine learning deployments.
- Enhance your professional credibility and significantly boost your career prospects in the rapidly expanding field of cloud-based machine learning.
- Be equipped with the practical skills and theoretical knowledge to confidently design and implement robust ML architectures that address complex, real-world business challenges.
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PROS
- Comprehensive Exam Preparation: Covers all domains of the AWS Certified Machine Learning Specialty exam, ensuring thorough readiness with integrated theory, practical labs, and practice questions.
- Strong Hands-On Focus: Abundant practical labs provide real-world experience, solidifying theoretical concepts and building confidence in AWS ML service usage.
- Up-to-Date Content: Recently updated (February 2026) to align with the latest AWS services and certification syllabus, ensuring relevant and current knowledge.
- High Student Satisfaction: A 4.50/5 rating from over 7,300 students indicates proven effectiveness and quality instruction.
- Flexible Learning: Downloadable PDF slides and 25.8 hours of content allow for self-paced study and efficient revision.
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CONS
- Time Commitment: Mastering the extensive material and hands-on labs requires a dedicated and significant time investment.
Learning Tracks: English,IT & Software,IT Certifications
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