The Machine Learning Pipeline on AWS - Build, Train, and Deploy ML Models
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The Machine Learning Pipeline on AWS

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The Machine Learning Pipeline on AWS is a comprehensive course that teaches you how to build, train, and deploy machine learning models on AWS. It covers key concepts such as data preparation, feature engineering, model training, and deployment using services like Amazon SageMaker. Through hands-on exercises, you'll learn to create scalable, secure, and efficient machine learning workflows on the AWS cloud.

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Overview

In a project-based learning setting, this course investigates how to apply the iterative machine learning (ML) process pipeline to resolve a real-world business issue. Through instructor presentations and demonstrations, students will get an understanding of each stage of the process pipeline. They will then use this knowledge to finish a project that addresses one of three business problems: airline delays, recommendation engines, or fraud detection. Students will have effectively developed, trained, assessed, adjusted, and implemented an ML model using Amazon SageMaker that addresses their chosen business challenge at the end of the course. This course will be helpful for students who have little to no expertise with or understanding of machine learning. A basic understanding of statistics will be beneficial. 

Key Features

  • 1. Comprehensive ML Training: Learn to build, train, and deploy machine learning models on AWS.
  • 2. Core AWS Tools: Utilize Amazon SageMaker and other AWS services for ML workflows.
  • 3. Data Preparation: Master techniques for data cleaning, transformation, and feature engineering.
  • 4. Model Training: Train and fine-tune ML models using scalable AWS infrastructure.
  • 5. Deployment and Monitoring: Deploy ML models securely and monitor their performance in production.
  • 6. Scalable Workflows: Design machine learning pipelines that are efficient and scalable.
  • 7. Hands-On Labs: Gain practical experience through real-world scenarios and exercises.
  • 8. Security Best Practices: Learn to secure data and ML models on the AWS cloud.
  • 1. Comprehensive ML Training: Learn to build, train, and deploy machine learning models on AWS.
  • 2. Core AWS Tools: Utilize Amazon SageMaker and other AWS services for ML workflows.
  • 3. Data Preparation: Master techniques for data cleaning, transformation, and feature engineering.
  • 4. Model Training: Train and fine-tune ML models using scalable AWS infrastructure.
  • 5. Deployment and Monitoring: Deploy ML models securely and monitor their performance in production.
  • 6. Scalable Workflows: Design machine learning pipelines that are efficient and scalable.
  • 7. Hands-On Labs: Gain practical experience through real-world scenarios and exercises.
  • 8. Security Best Practices: Learn to secure data and ML models on the AWS cloud.

Curriculum

Study Plan

1Module 1: Introduction to Machine Learning and the ML Pipeline
  • Overview of machine learning, including use cases, types of machine learning, and key concepts
  • Overview of the ML pipeline 
  • Introduction to course projects and approach  
1Module 1: Introduction to Machine Learning and the ML Pipeline
  • Overview of machine learning, including use cases, types of machine learning, and key concepts
  • Overview of the ML pipeline 
  • Introduction to course projects and approach  
2Module 2: Introduction to Amazon SageMaker
  • Introduction to Amazon SageMaker 
  • Demo: Amazon SageMaker and Jupyter notebooks 
  • Lab: Amazon SageMaker and Jupyter notebooks 
2Module 2: Introduction to Amazon SageMaker
  • Introduction to Amazon SageMaker 
  • Demo: Amazon SageMaker and Jupyter notebooks 
  • Lab: Amazon SageMaker and Jupyter notebooks 
3Module 3: Problem Formulation
  • Overview of problem formulation and deciding if ML is the right solution
  • Converting a business problem into an ML problem 
  • Amazon SageMaker Ground Truth
  • Practice problem formulation 
  • Formulate problems for projects 
3Module 3: Problem Formulation
  • Overview of problem formulation and deciding if ML is the right solution
  • Converting a business problem into an ML problem 
  • Amazon SageMaker Ground Truth
  • Practice problem formulation 
  • Formulate problems for projects 
4Module 4: Preprocessing
  • Overview of data collection and integration, and techniques for data preprocessing and visualization 
  • Practice preprocessing 
  • Preprocess project data 
4Module 4: Preprocessing
  • Overview of data collection and integration, and techniques for data preprocessing and visualization 
  • Practice preprocessing 
  • Preprocess project data 
5Module 5: Model Training
  • Choosing the right algorithm 
  • Formatting and splitting your data for training 
  • Loss functions and gradient descent for improving your model 
5Module 5: Model Training
  • Choosing the right algorithm 
  • Formatting and splitting your data for training 
  • Loss functions and gradient descent for improving your model 
6Module 6: Model Evaluation
  • How to evaluate classification models
  • How to evaluate regression models 
  • Practice model training and evaluation
  • Train and evaluate project models 
  • Initial project presentations
6Module 6: Model Evaluation
  • How to evaluate classification models
  • How to evaluate regression models 
  • Practice model training and evaluation
  • Train and evaluate project models 
  • Initial project presentations
7Module 7: Feature Engineering and Model Tuning
  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning 
  • SageMaker hyperparameter optimization 
  • Practice feature engineering and model tuning 
  •  Apply feature engineering and model tuning

7Module 7: Feature Engineering and Model Tuning
  • Feature extraction, selection, creation, and transformation
  • Hyperparameter tuning 
  • SageMaker hyperparameter optimization 
  • Practice feature engineering and model tuning 
  •  Apply feature engineering and model tuning

8Module 8: Deployment
  • How to deploy, inference, and monitor your model on Amazon SageMaker 
  • Deploying ML at the edge 
  • Demo: Creating an Amazon SageMaker endpoint 
  • Post-assessment
  • Course wrap-up 
8Module 8: Deployment
  • How to deploy, inference, and monitor your model on Amazon SageMaker 
  • Deploying ML at the edge 
  • Demo: Creating an Amazon SageMaker endpoint 
  • Post-assessment
  • Course wrap-up 

About Instructor

  • 3000+ Learner Trained
  • 42+ Corporate Recruiting Partners & 20+ College Partners
  • 1000+ Review
  • 200+ Classes/Month
  • aws solution architect certified
  • 3000+ Learner Trained
  • 42+ Corporate Recruiting Partners & 20+ College Partners
  • 1000+ Review
  • 200+ Classes/Month
  • aws solution architect certified
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