Machine Learning Pipeline on AWS Training Singapore.

The Machine Learning Pipeline on AWS Course Overview

Overview

Enroll for 3-day The Machine Learning Pipeline on AWS training course from this course Solutions accredited by AWS. The Machine Learning Pipeline on AWS course developers will learn to solve real world business problems using it.

Through a blend of hands-on labs and interactive lectures, participants will learn best practices for problem solving using Amazon Sagemaker and be able to select and justify the appropriate ML approach for a given business problem.

Target Audience:

Candidates working as Developer.

Candidates working as Solutions Architect.

Candidates working as Data engineer.

You will learn:

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

Hands-on: Amazon SageMaker and Jupyter notebooks

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

Practice problem formulation

Formulate problems for projects

Module 4: Preprocessing

Overview of data collection and integration, and techniques for data preprocessing and visualization

Practice preprocessing

Preprocess project data

Class discussion about projects

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

Module 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

Module 7: Feature Engineering and Model Tuning

Feature extraction, selection, creation, and transformation

Hyperparameter tuning

Demo: SageMaker hyperparameter optimization

Practice feature engineering and model tuning

Apply feature engineering and model tuning to projects

Final project presentations

Module 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