The Machine Learning Pipeline on AWS course is designed to provide learners with in-depth, hands-on training in building, training, and deploying machine learning models using Amazon Web Services. This course takes participants through every stage of the ML lifecycle—from problem formulation and data preprocessing to model evaluation and deployment—using AWS tools such as Amazon SageMaker.

By the end of the course, learners will gain both theoretical knowledge and practical experience with the AWS ML pipeline, equipping them to tackle real-world ML challenges in production environments.

The course covers key areas such as:

• Machine Learning Fundamentals: Understand ML use cases, types (supervised, unsupervised), and essential concepts that drive predictive models.
• ML Pipeline Overview: Learn the structure and stages of the machine learning pipeline, including data handling, model development, and deployment.
• Amazon SageMaker & Jupyter Notebooks: Gain hands-on experience using Amazon SageMaker within Jupyter Notebooks to create, test, and tune ML models.
• Problem Formulation: Learn how to frame business challenges into machine learning problems and select the appropriate ML approaches.
• Data Preprocessing & Labeling: Use Amazon SageMaker Ground Truth for data labeling and explore best practices in cleaning and transforming data for model readiness.
• Model Training & Algorithm Selection: Choose and train models using SageMaker, with an understanding of loss functions and optimization strategies.
• Model Evaluation: Evaluate model accuracy and performance using industry-standard metrics for both classification and regression tasks.
• Feature Engineering & Model Tuning: Perform advanced feature extraction, selection, and transformation while leveraging hyperparameter tuning to optimize performance.
• Model Deployment & Monitoring: Learn to deploy models using SageMaker, perform inference, monitor model behavior, and deploy at the edge.
• Course Project & Assessment: Apply your knowledge to a capstone project and complete a post-course assessment to validate your skills.

Course Prerequisites

To ensure success in this course, participants should meet the following prerequisites:

• Basic understanding of machine learning concepts (e.g., supervised vs. unsupervised learning)
• Proficiency in Python programming
• Familiarity with AWS services, especially Amazon SageMaker
• Experience using Jupyter Notebooks or similar interactive development environments
• Foundational skills in data preprocessing and manipulation
• Analytical thinking and problem-solving mindset for ML applications

These prerequisites prepare participants to maximize their learning and effectively engage with hands-on AWS ML exercises.

Target Audience

The Machine Learning Pipeline on AWS course is ideal for professionals and students interested in designing, implementing, and deploying ML solutions using AWS, including:

• Data Scientists
• Machine Learning Engineers
• Data Engineers
• Software Developers interested in ML
• IT Professionals deploying ML on AWS
• AWS Cloud Practitioners
• Cloud Solutions Architects
• Technical Project Managers overseeing ML initiatives
• Business Analysts interested in ML workflows
• AI/ML Consultants and Researchers
• Data Analysts transitioning to machine learning
• Product Managers adopting ML for product innovation
• Students and Educators in ML and data science fields

Why Choose us

⭢ Live Online Training (Duration : 32 Hours)

⭢ Guaranteed to run classes

⭢ Experienced & certified trainers

⭢ Query Handling session

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Learning Objectives

After completing the Machine Learning Pipeline on AWS course, learners will be able to:

• Understand the complete ML lifecycle and its implementation on AWS
• Launch Amazon SageMaker and utilize Jupyter Notebooks for ML development
• Formulate real-world problems into viable ML challenges
• Prepare and preprocess datasets using SageMaker tools
• Apply suitable algorithms and training strategies for various use cases
• Evaluate model performance and interpret classification/regression outputs
• Engineer features and perform hyperparameter tuning for optimization
• Deploy ML models on Amazon SageMaker, including at the edge
• Perform inference and monitor deployed models for data drift and performance
• Build end-to-end ML pipelines and complete real-world ML projects on AWS