Course Overview

Machine Learning Engineering on AWS helps learners build the skills required to design, build, train, tune, deploy, monitor, and maintain machine learning solutions on AWS. The course focuses on the full ML lifecycle, including data preparation, feature engineering, model development, Amazon SageMaker, model deployment, MLOps, CI/CD, monitoring, security, and performance optimization.

This training is suitable for learners preparing for AWS machine learning engineering roles and the AWS Certified Machine Learning Engineer Associate certification. AWS lists the certification as Associate level, with a 130-minute exam, 65 questions, and a USD 150 exam fee.

After completing Machine Learning Engineering on AWS participants will be able to:

  • Summarize core machine learning concepts, techniques, and their applications within the AWS ecosystem.
  • Discuss appropriate use cases for supervised, unsupervised, and deep learning models based on business and technical requirements.
  • Describe how to prepare, process, and transform data using AWS services for machine learning workflows.
  • Recognize suitable algorithms and modeling approaches aligned with interpretability and performance goals.
  • Explain how to build, train, and evaluate models using Amazon SageMaker and related AWS tools.
  • Design and implement scalable ML pipelines for training, deployment, and monitoring.
  • Implement MLOps practices, including CI/CD workflows and model lifecycle automation.
  • Apply security and compliance best practices for ML workloads on AWS.
  • Monitor deployed models effectively and apply techniques for detecting data and concept drift.

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Key Features of Machine Learning Engineering on AWS:

  • Comprehensive Coverage
    Covers the full ML lifecycle — from data preparation to model monitoring on AWS.

  • Hands-On Labs
    Gain practical experience through labs focused on SageMaker, feature engineering, model training, tuning, and deployment.

  • Real-World Scenarios
    Learn through practical use cases such as fraud detection, sentiment analysis, and recommendation systems.

  • Performance Optimization
    Discover techniques for deploying machine learning models in a cost-effective and low-latency manner on AWS.

  • CI/CD for ML
    Implement MLOps by integrating SageMaker Pipelines with AWS CodePipeline to automate the ML workflow.

Who Should Attend:

  • Machine Learning Engineers
  • Data Scientists
  • DevOps Engineers working on ML workloads
  • Software Developers with ML responsibilities

Prerequisites of Machine Learning Engineering on AWS:

  • Familiarity with basic machine learning concepts
  • Working knowledge of Python programming language and common data science libraries such as NumPy, Pandas, and Scikit-learn
  • Basic understanding of cloud computing concepts and familiarity with AWS
  • Experience with version control systems such as Git (beneficial but not required)

Learning Objectives of Machine Learning Engineering on AWS

  • Explain ML fundamentals and its applications in the AWS Cloud.
  • Process, transform, and engineer data for ML tasks by using AWS services.
  • Select appropriate ML algorithms and modeling approaches based on problem requirements and model interpretability.
  • Design and implement scalable ML pipelines by using AWS services for model training, deployment, and orchestration.
  • Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.
  • Discuss appropriate security measures for ML resources on AWS.
  • Implement monitoring strategies for deployed ML models, including techniques for detecting data drift.

Why choose CloudThat as your training partner?

  • Expert Instructors: CloudThat's courses are led by AWS Authorized Instructor with extensive experience in AI and AWS, ensuring high-quality instruction.
  • Hands-On Learning: Emphasis on practical, hands-on labs and real-world demos to provide participants with valuable, applicable skills.
  • Comprehensive Curriculum: Courses cover a wide range of topics, from introductory concepts to advanced implementation, offering a well-rounded learning experience.
  • Focus on Responsible AI: Training includes responsible AI principles, ensuring ethical practices and compliance with industry standards.
  • Career Advancement: CloudThat's training programs are designed to enhance career prospects, preparing participants for various roles in AI and machine learning.
  • Post-Training Support: CloudThat offers post-training support, additional resources, and forums for ongoing learning and development

Course Outline Download Course Outline

  • Topic A: Introduction to ML
  • Topic B: Amazon SageMaker AI
  • Topic C: Responsible ML

  • Topic A: Evaluating ML business challenges
  • Topic B: ML training approaches
  • Topic C: ML training algorithms

  • Topic A: Data preparation and types
  • Topic B: Exploratory data analysis
  • Topic C: AWS storage options and choosing storage

  • Topic A: Handling incorrect, duplicated, and missing data
  • Topic B: Feature engineering concepts
  • Topic C: Feature selection techniques
  • Topic D: AWS data transformation services
  • Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR
  • Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

  • Topic A: Amazon SageMaker AI built-in algorithms
  • Topic B: Selecting built-in training algorithms
  • Topic C: Amazon SageMaker Autopilot
  • Topic D: Model selection consideration
  • Topic E: ML cost considerations

  • Topic A: Model training concepts
  • Topic B: Training models in Amazon SageMaker AI
  • Lab 3: Training a model with Amazon SageMaker AI

  • Topic A: Evaluating model performance
  • Topic B: Techniques to reduce training time Topic C: Hyperparameter tuning techniques
  • Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI

  • Topic A: Deployment considerations and target options
  • Topic B: Deployment strategies
  • Topic C: Choosing a model inference strategy
  • Topic D: Container and instance types for inference
  • Lab 5: Shifting Traffic A/B

  • Topic A: Access control
  • Topic B: Network access controls for ML resources
  • Topic C: Security considerations for CI/CD pipelines

  • Topic A: Introduction to MLOps
  • Topic B: Automating testing in CI/CD pipelines
  • Topic C: Continuous delivery services
  • Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio

  • Topic A: Detecting drift in ML models
  • Topic B: SageMaker Model Monitor
  • Topic C: Monitoring for data quality and model quality Topic D: Automated remediation and troubleshooting Lab 7: Monitoring a Model for Data Drift

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Course ID: 25229

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FAQs:

Completing this training can prepare you for roles such as Machine Learning Engineer, AI/ML Developer, Data Scientist, MLOps Engineer, AI Solutions Architect, and Applied Scientist, among others.

The average salary for machine learning professionals in India ranges from ₹8 to ₹15 lakhs per annum at the entry level, with senior professionals and specialists earning ₹25 to ₹50 lakhs or more, depending on experience and role complexity.

You will gain in-depth knowledge of ML workflows, data engineering, model selection and training, hyperparameter tuning, deployment on AWS, and MLOps practices. You will also work hands-on with services like Amazon SageMaker, AWS Lambda, S3, Glue, and CloudWatch.

This course equips you with real-world experience and the technical depth needed to deploy scalable ML solutions on AWS, making you a strong candidate for advanced roles and certifications in machine learning and cloud AI.

While not mandatory, it is recommended that participants have a basic understanding of machine learning concepts, Python programming, and AWS fundamentals. Prior experience with data analysis or model building will be helpful.

Yes. Participants receive post-training support, including access to discussion forums, practice tests, lab resources, and mentorship opportunities to help with skills development and job readiness.

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