The Machine Learning Pipeline on AWS

Course Modules

Day 1:

1. Module 1: Introduction to Machine Learning and the ML Pipeline

   • Overview of machine learning, including use cases, types of machine learning, and key concepts.
   • Introduction to the ML pipeline and its significance in developing ML solutions.
   • Discussion on formulating business problems into ML problems.

2. Module 2: Introduction to Amazon SageMaker

   • Overview of Amazon SageMaker and its role in the ML lifecycle.
   • Hands-on session with SageMaker and Jupyter notebooks for data exploration and model development.

3. Module 3: Problem Formulation

   • Techniques for converting business problems into ML problems.
   • Using Amazon SageMaker Ground Truth for data labeling.
   • Practical exercises on problem formulation and project selection.

Day 2:

1. Module 4: Preprocessing

   • Data preprocessing techniques, including data cleaning and transformation.
   • Utilizing SageMaker Data Wrangler for data processing workflows.
   • Hands-on exercises in preparing data for model training.

2. Module 5: Model Training

   • Strategies for selecting appropriate ML algorithms.
   • Training models using SageMaker's built-in algorithms and custom scripts.
   • Managing training jobs and monitoring progress.

3. Module 6: Model Evaluation

   • Evaluating model performance using various metrics.
   • Implementing cross-validation techniques.
   • Using SageMaker's model evaluation tools.

Day 3:

1. Module 7: Feature Engineering and Model Tuning

   • Techniques for feature selection and engineering to improve model performance.
   • Hyperparameter tuning using SageMaker's automatic model tuning capabilities.
   • Addressing model overfitting and underfitting.

2. Module 8: Deployment

   • Deploying models for real-time and batch inference.
   • Setting up endpoints and managing deployments.
   • Monitoring deployed models and managing updates.

Course Highlights:

• Hands-on labs and demonstrations to reinforce learning.
• Best practices for designing scalable, cost-optimized, and secure ML pipelines on AWS.
• Preparation for applying ML techniques to real-world business problems.