AWS-MLENG | Machine Learning Engineering on AWS
 
Machine Learning (ML) Engineering on Amazon Web Services (AWS) is a 3-day intermediate course designed for ML professionals seeking to learn machine learning engineering on AWS. Participants learn to build, deploy, orchestrate, and operationalize ML solutions at scale through a balanced combination of theory, practical labs, and activities. Participants will gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such as Amazon EMR to develop robust, scalable, and production-ready machine learning applications.
 
Course objectives
In this course, you will learn to do the following:
Explain ML fundamentals and its applications in the AWS Cloud.
Process, transform, and engineer data for ML tasks by using AWS services.
Select appropriate ML algorithms and modeling approaches based on problem requirements and model interpretability.
Design and implement scalable ML pipelines by using AWS services for model training, deployment, and orchestration.
Create automated continuous integration and delivery (CI/CD) pipelines for ML workflows.
Discuss appropriate security measures for ML resources on AWS.
Implement monitoring strategies for deployed ML models, including techniques for detecting data drift

Intended audience
This course is designed for professionals who are interested in building, deploying, and operationalizing machine learning models on AWS. This could include current and in-training machine learning engineers who might have little prior experience with AWS. Other roles that can benefit from this training are DevOps engineer, developer, and SysOps engineer.

Prerequisites
We recommend that attendees of this course have the following:
Familiarity with basic machine learning concepts.
Working knowledge of Python programming language and common data science libraries such as NumPy, Pandas, and Scikit-learn.
Basic understanding of cloud computing concepts and familiarity with AWS.
Experience with version control systems such as Git (beneficial but not required).

Course Outline
Day 1
Module 0: Course Introduction
Module 1: Introduction to Machine Learning (ML) on AWS
Module 2: Analyzing Machine Learning (ML) Challenges
Module 3: Data Processing for Machine Learning (ML)
Module 4: Data Transformation and Feature Engineering
Day 2
Module 5: Choosing a Modeling Approach
Module 6: Training Machine Learning (ML) Models
Module 7: Evaluating and Tuning Machine Learning (ML) models
Module 8: Model Deployment Strategies
Day 3
Module 9: Securing AWS Machine Learning (ML) Resources
Module 10: Machine Learning Operations (MLOps) and Automated Deployment
Module 11: Monitoring Model Performance and Data Quality
Module 12: Course Wrap-up
 
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Debido a las constantes actualizaciones de los contenidos de los cursos por parte del fabricante, el contenido de este temario puede variar con respecto al publicado en el sitio oficial, sin embargo, Netec siempre entregará la versión actualizada de éste