The Machine Learning Pipeline on AWS Training

The Machine Learning Pipeline on AWS Training demonstrates the methodologies to utilize machine learning pipeline to rectify real business issues in a project-based learning environment. This technical course helps professionals gain comprehensive knowledge on each phase of the pipeline from the demonstrations and preparations and utilizing that information to finish a project. It trains individuals to build, evaluate, train, and launch an ML model with the help of Amazon SageMaker that rectifies their major business issue.

This course teaches you to apply machine learning to a real-time issue and use the ML pipeline to overcome a specific business problem. You will demonstrate some of the leading practices for creating scalable, cost-effective, and protection ML pipelines in AWS. It enables you to choose and justify the suitable ML approach for a stated business problem. This course is ideal for developers, solution engineers, or anyone who wants to gain more information related to ML pipeline using Amazon SageMaker.

• Justifying and selecting appropriate ML approaches for particular business difficulties.
• Utilizing ML pipeline to solve the specified business problems.
• Utilizing the ML pipeline to resolve an exact business troubles.
• Deploying, Training, evaluating, and tuning a ML model in Amazon SageMaker.
• Describing few most excellent training so as to secure ML pipelines, cost-optimized, designing scalable in AWS.
• Applying ML to real-life business difficulties after the competition of the course.

The Machine Learning Pipeline On AWS Training is highly advantageous for Data Engineers, Solutions Architects and Developers have little experience with Machine learning fundamentals and ML pipelines. Therefore, professionals who are utilizing Amazon SageMaker and have brief knowledge about SageMaker can enroll in this training.

• 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
View Less    View More   
2. Introduction to Amazon SageMaker
Introduction to Amazon SageMaker
Demo: Amazon SageMaker and Jupyter notebooks
Lab: Introduction to Amazon SageMaker
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
4. Preprocessing
Overview of data collection and integration, and techniques for data preprocessing and visualization
Lab: Data Preprocessing (including project work)
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
6. Model Training
How to evaluate classification models
How to evaluate regression models
Practice model training and evaluation
Train and evaluate project models
Lab: Model Training and Evaluation (including project work)
Project Share-Out 1
7. Feature Engineering and Model Tuning
Feature extraction, selection, creation, and transformation
Hyperparameter tuning
Demo: SageMaker hyperparameter optimization
Lab: Feature Engineering (including project work)
8. Module Deployment
How to deploy, inference, and monitor your model on Amazon SageMaker
Deploying ML at the edge
9. Course Wrap-Up
Project Share-Out 2
Post-Assessment
Wrap-up