The Machine Learning Pipeline on AWS Training Logo

The Machine Learning Pipeline on AWS Training

Live Online & Classroom Enterprise Training

This course provides a practical understanding of how to design, build, automate, and deploy end-to-end machine learning pipelines using AWS services such as Amazon SageMaker, S3, Lambda, and CI/CD tools.

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What is The Machine Learning Pipeline on AWS Course about?

This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.

What are the objectives of The Machine Learning Pipeline on AWS Course ?

  • Understand the end-to-end ML lifecycle on AWS
  • Build and orchestrate ML pipelines using Amazon SageMaker
  • Automate training and deployment using CI/CD practices
  • Implement model monitoring and performance tracking
  • Apply security and cost-optimization best practices

Who is The Machine Learning Pipeline on AWS Course for?

  • Data Scientists
  • Machine Learning Engineers
  • Cloud Engineers
  • DevOps professionals working with ML workloads
  • Developers interested in AI/ML on AWS

What are the prerequisites for The Machine Learning Pipeline on AWS Course?

Prerequisites:
  • Basic knowledge of machine learning concepts
  • Familiarity with Python programming
  • Understanding of AWS core services (EC2, S3, IAM)
  • Basic knowledge of cloud computing concepts
  • Experience with data handling and analysis

Learning Path:
  • Introduction to ML on AWS and SageMaker
  • Data preparation and feature engineering pipelines
  • Model training, tuning, and evaluation
  • Deployment and automation with MLOps practices
  • Monitoring, governance, and optimization of ML workflows

Related Courses:
  • Introduction to Machine Learning on AWS
  • AWS Data Engineering Fundamentals
  • MLOps Essentials with AWS
  • Advanced Amazon SageMaker Workshop

Available Training Modes

Live Online Training

4 Days

Course Outline Expand All

Expand All

  • What is Machine Learning and its applications
  • Types of ML: Supervised, Unsupervised, Reinforcement
  • End-to-end ML pipeline overview
  • Data, features, models, and predictions
  • Common challenges in ML projects
  • Overview of Amazon SageMaker services
  • SageMaker Studio and notebooks
  • Built-in algorithms and frameworks
  • Training and inference workflow
  • Pricing and cost considerations
  • Understanding business requirements
  • Converting business problems into ML problems
  • Selecting appropriate ML approach
  • Defining inputs, outputs, and constraints
  • Success criteria and evaluation metrics
  • Data cleaning techniques
  • Handling missing values
  • Encoding categorical variables
  • Data normalization and scaling
  • Splitting data into train, validation, and test sets
  • Selecting algorithms for training
  • Using SageMaker training jobs
  • Hyperparameters overview
  • Managing training data in S3
  • Monitoring training performance
  • Evaluation metrics (accuracy, precision, recall, etc.)
  • Confusion matrix interpretation
  • Cross-validation basics
  • Bias and variance analysis
  • Model comparison techniques
  • Feature selection techniques
  • Creating new features from raw data
  • Feature importance analysis
  • Hyperparameter tuning with SageMaker
  • Avoiding overfitting and underfitting
  • Model deployment options in SageMaker
  • Real-time vs batch inference
  • Creating and managing endpoints
  • Monitoring deployed models
  • Model versioning and updates

Who is the instructor for this training?

The trainer for this The Machine Learning Pipeline on AWS Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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