Course Overview

This training is designed to prepare learners for the Machine Learning Engineering on AWS. It focuses on developing end-to-end ML solutions on AWS. Learners will explore problem framing, data engineering, model training and evaluation, deployment, monitoring, and optimization. Through hands-on labs, real-world scenarios, and exam-focused guidance, students will gain practical ML skills and a thorough understanding of AWS services like SageMaker, S3, Glue, and CloudWatch.

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 industry experts 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, mock tests, lab resources, and mentorship opportunities to help with certification prep and job readiness.

    Enquire Now