Machine Learning Pipelines on AWS Eğitimi

We recommend that attendees of this course have the following prerequisites:

• Basic knowledge of Python programming language
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
• Basic experience working in a Jupyter notebook environment

In this course, you will learn how to:

• Select and justify the appropriate ML approach for a given business problem
• Use the ML pipeline to solve a specific business problem
• Train, evaluate, deploy, and tune an ML model in Amazon SageMaker
• Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
• Apply machine learning to a real-life business problem after the course is complete

Module 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

Module 2: Introduction to Amazon SageMaker

• Introduction to Amazon SageMaker
• Demo: Amazon SageMaker and Jupyter notebooks
• Lab 1: Introduction to Amazon SageMaker

Module 3: Problem Formulation

• Overview of problem formulation and deciding if ML is the right solution
• Converting a business problem into an ML problem
• Demo: Amazon SageMaker Ground Truth
• Hands-on: Amazon SageMaker Ground Truth
• Problem Formulation Exercise and Review
• Project work for Problem Formulation

Day Two

Recap and Checkpoint #1

Module 4: Preprocessing

• Overview of data collection and integration, and techniques for data preprocessing and visualization
• Lab 2: Data Preprocessing (including project work)

Module 5: Model Training

• Choosing the right algorithm
• Formatting and splitting your data for training
• Loss functions and gradient descent for improving your model
• Demo: Create a training job in Amazon SageMaker

Day Three

Recap and Checkpoint #2

Module 6: Model Training

• How to evaluate classification models
• How to evaluate regression models
• Practice model training and evaluation
• Train and evaluate project models
• Lab 3: Model Training and Evaluation (including project work)
• Project Share-Out 1

Module 7: Feature Engineering and Model Tuning

• Feature extraction, selection, creation, and transformation
• Hyperparameter tuning
• Demo: SageMaker hyperparameter optimization

Day Four

Lab 4: Feature Engineering (including project work)

Recap and Checkpoint #3

Module 8: Module Deployment

• How to deploy, inference, and monitor your model on Amazon SageMaker
• Deploying ML at the edge

Module 9: Course Wrap-Up

• Project Share-Out 2
• Post-Assessment
• Wrap-up