Machine Learning Pipelines on AWS Training

Machine Learning Pipelines on AWS Training

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It is a program that will allow you to respond to the needs of the future, develop strategies for analyzing data; prepare data for analysis; explore and visualize data; create models with data using Python programming language, distribute them to applications and software and process them.

This course explores how to use machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Participants will learn each stage of the pipeline from the instructor and then apply this knowledge to complete a project that can solve business problems. By the end of the course, all participants will be able to successfully build a machine learning model using Amazon SageMaker to solve their chosen business problem.

After completing this course, you will learn how to apply machine learning to a real-life business problem, including how to select the appropriate machine learning approach for a specific business problem, how to use a machine learning framework to solve a specific business problem, how to orchestrate, evaluate, deploy, and tune a machine learning model in Amazon SageMaker, and some of the best practices for designing scalable, cost-optimized, and secure machine learning pipelines in AWS.

About Education

  • Module 1: Introduction to Machine Learning and ML Pipeline

Overview of machine learning, including use cases, types of machine learning and key concepts

Machine learning pipeline overview

Introduction and approach to sample projects

  • Module 2: Introduction to Amazon SageMaker

Introduction to Amazon SageMaker

Amazon SageMaker and Jupyter notebooks

  • Module 3: Problem Formulation

Overview of problem formulation and deciding whether machine learning is the right solution

Transforming a business problem into a machine learning problem

Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth

Problem Formulation Exercise and Review

Project work for Problem Formulation

  • Module 4: Preprocessing

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

Data Preprocessing

  • Module 5: Model Training

Choosing the right algorithm

Format and split your data for education

Loss functions and gradient descent to improve your model

Create an education job in Amazon SageMaker

  • Module 6: Model Training

How are classification models evaluated?

How to evaluate regression models?

Practice model training and evaluation

Model Training and Evaluation

  • Module 7: Feature Engineering and Model Tuning

Feature extraction, selection, creation and transformation

Hyperparameter setting

SageMaker hyperparameter optimization

Feature Engineering

  • Module 8: Module Deployment

Deploy, infer and monitor models in Amazon SageMaker

Deploying edge machine learning

Who Should Receive the Training?

  • Developers
  • Solution Architects
  • Data Engineers
  • Anyone interested in Artificial Intelligence and Machine Learning who wants to learn the applications of Machine Learning concepts with AWS can participate.


We recommend that they have the following prerequisites deemed necessary for this training

  • Basic information about Python programming language
  • Basic information about AWS Cloud infrastructure
  • Basics of working in a Jupyter notebook environment

Plan this training institutionally!

This training can be planned in different durations and content specific to your organization. Please contact us for detailed rich content and planning to realize your training objectives.

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