AWS Certified Machine Learning Pipeline- Specialty

AWS Certified Machine Learning Pipeline- Specialty

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Duration

4 Days

Certified Instructor

Course Id

nextecAW907

Course Description

Course Overview

The AWS Certified Machine Learning Pipeline – Specialty course is designed to equip individuals with the knowledge and skills required to design, build, and deploy scalable machine learning (ML) solutions on the Amazon Web Services (AWS) platform. This course focuses on the end-to-end process of developing ML pipelines, including data preparation, model training, evaluation, and deployment. Participants will learn how to leverage AWS services and tools to create efficient and reliable ML pipelines that can handle large-scale data and complex ML models.

Prerequisites

To enroll in the AWS Certified Machine Learning Pipeline – Specialty course, participants should have:

  • Experience with AWS services, particularly those related to machine learning, such as Amazon SageMaker and AWS Glue
  • Knowledge of machine learning concepts and algorithms
  • Proficiency in at least one programming language, preferably Python
  • Familiarity with data preprocessing, feature engineering, and model evaluation techniques

Methodology: The course employs a combination of instructor-led training, interactive discussions, demonstrations, and hands-on labs. Participants will engage in lectures, real-world examples, and interactive activities to understand the concepts and best practices of building ML pipelines on AWS. They will also work on hands-on labs and projects to gain practical experience in designing and implementing ML pipelines using AWS services.

Course Outline

Introduction to Machine Learning Pipelines

Overview of ML pipeline architecture and components

Understanding the AWS ML services and tools available for building pipelines

Exploring common challenges and considerations in ML pipeline development

Data Preparation and Feature Engineering

Collecting and preprocessing data using AWS services like AWS Glue and Amazon Athena

Applying feature engineering techniques to improve data quality and model performance

Handling large-scale datasets and distributed computing with AWS services

Model Training and Evaluation

Designing and implementing model training workflows with Amazon SageMaker

Evaluating model performance and selecting appropriate evaluation metrics

Understanding techniques for model validation and hyperparameter tuning

Model Deployment and Monitoring

Deploying ML models as scalable and reliable services with Amazon SageMaker

Implementing model monitoring and handling concept drift

Managing and automating model retraining and versioning

Security, Compliance, and Governance

Implementing security measures to protect data and models

Ensuring compliance with regulatory requirements and industry standards

Establishing governance and monitoring mechanisms for ML pipelines

Course Outcome

Upon completion of the AWS Certified Machine Learning Pipeline – Specialty course, participants will:

  • Have a deep understanding of the concepts, techniques, and best practices for building ML pipelines on AWS
  • Possess the skills to design, develop, and deploy scalable and reliable ML solutions using AWS services
  • Be prepared to pass the AWS Certified Machine Learning Pipeline – Specialty exam
  • Have the ability to contribute to ML pipeline development projects, automate model training and deployment, and monitor ML systems
  • Understand security, compliance, and governance considerations in ML pipeline development on AWS

Labs

The course includes hands-on labs and exercises that provide participants with practical experience in building ML pipelines on AWS. Participants will have access to AWS resources and tools to complete the labs, allowing them to practice data preparation, feature engineering, model training, and deployment using AWS services like Amazon SageMaker and AWS Glue. The labs are designed to reinforce the concepts covered in the course and develop proficiency in building ML pipelines on AWS.

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