AWS

AWS CERTIFIED MACHINE LEARNING - SPECIALTY (MLS-C01)

The AWS Certified Machine Learning - Specialty course is designed for professionals who design, build, train, deploy, and maintain machine learning (ML) solutions on Amazon Web Services (AWS). This course focuses on the end-to-end machine learning lifecycle, including data preparation, model development, training, tuning, deployment, monitoring, and optimization using AWS ML services.

Participants will gain hands-on knowledge of selecting appropriate ML algorithms, designing scalable ML architectures, and implementing production-ready ML solutions. The course prepares learners for the AWS Certified Machine Learning - Specialty (MLS-C01) certification exam.

Course Objectives

By the end of this course, participants will be able to:

  • Design end-to-end machine learning solutions on AWS

  • Prepare, clean, and engineer data for ML workloads

  • Select appropriate ML algorithms and frameworks

  • Train, tune, and evaluate ML models at scale

  • Deploy and monitor ML models in production

  • Implement security, governance, and cost optimization for ML systems

  • Prepare effectively for the AWS Machine Learning – Specialty exam

Course Curriculum

1

    • Machine learning concepts and use cases
    • Supervised, unsupervised, and reinforcement learning
    • AWS ML ecosystem overview

2

  • Data collection and ingestion
  • Data preparation and feature engineering
  • Amazon S3 data lakes
  • AWS Glue for data transformation

3

  • Data analysis techniques
  • Amazon Athena
  • Amazon QuickSight
  • Identifying data quality issues

4

  • Choosing ML algorithms
  • Built-in algorithms vs custom models
  • Amazon SageMaker training jobs
  • Distributed training and scaling

5

  • Hyperparameter optimization
  • Model evaluation metrics
  • Bias, variance, and overfitting
  • Model selection strategies

6

  • Batch vs real-time inference
  • Amazon SageMaker endpoints
  • Model versioning and A/B testing
  • Blue/green deployments for ML models

7

  • Model monitoring and drift detection
  • Amazon CloudWatch and logs
  • CI/CD for ML (MLOps concepts)
  • Retraining and automation pipelines

8

  • IAM for ML workloads
  • Data encryption and access control
  • Responsible AI and governance considerations

9

  • Cost-efficient ML training and inference
  • Instance selection and scaling strategies
  • Optimizing performance at scale

10

  • Natural Language Processing (NLP)
  • Computer Vision
  • Recommendation systems
  • Time-series forecasting

11

  • Exam domains and structure
  • Scenario-based practice questions
  • Exam tips and strategies

12

  • Machine learning engineers
  • Data scientists and data engineers
  • Cloud architects designing ML solutions
  • Software engineers working with ML workloads
  • Professionals preparing for AWS Machine Learning certification

13

  • AWS Certified Cloud Practitioner or equivalent knowledge
  • Strong understanding of ML fundamentals
  • Experience with Python and basic data science libraries
  • Familiarity with statistics and data analysis

14

  • 4-5 days (Instructor-led)
  • 32-40 hours of training

15

  • Instructor-led advanced technical sessions
  • Hands-on ML labs using Amazon SageMaker
  • Real-world ML use cases and scenarios
  • Exam-focused learning approach

16

  • This course prepares participants for the AWS Certified Machine Learning - Specialty (MLS-C01) certification exam

17

  • Comprehensive training materials
  • Hands-on lab guides
  • Practice exam questions
  • Certificate of course completion

This course includes

  • 17+ Activity Modules
  • 40 hours + lessons
  • Lifetime access
  • Certificate of completion
  • Available on desktop and mobile

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