The Machine Learning Pipeline on AWS Training

The Machine Learning Pipeline on AWS Course Overview

The Machine Learning Pipeline on AWS course is designed to equip learners with a comprehensive understanding of how to create and deploy machine learning models using AWS services. The curriculum is structured into coherent modules that guide students from the basics of machine learning to the complexities of Model deployment.

In Module 1, participants will grasp the fundamentals of machine learning, exploring various use cases, the types of machine learning, and key concepts. They will also get a thorough overview of the ML Pipeline and be introduced to the course projects.

Module 2 dives into Amazon SageMaker, providing an introduction and hands-on experience with Jupyter notebooks within the AWS environment.

Subsequent modules guide learners through Problem formulation, Data preprocessing, Model training with Amazon SageMaker, Model evaluation, and the intricacies of Feature engineering and Model tuning.

The final module, Module 8, covers the crucial aspects of deploying models on Amazon SageMaker, including Inference and Monitoring, as well as Deploying ML at the edge, culminating in a course wrap-up and post-assessment.

By the end of the course, participants will have a solid understanding of the ML Pipeline on AWS and practical experience that will empower them to tackle real-world machine learning challenges.

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Course Prerequisites

Certainly! Here are the minimum required prerequisites for successfully undertaking training in The Machine Learning Pipeline on AWS course:

Basic understanding of machine learning concepts, including supervised and unsupervised learning, and familiarity with common use cases.
Fundamental knowledge of Python programming, as it is commonly used in machine learning projects and hands-on exercises.
Familiarity with AWS services, particularly Amazon SageMaker, as the course includes lessons on utilizing this service for machine learning tasks.
Experience with Jupyter notebooks or similar interactive computing environments, as they will be used for demonstrations and hands-on activities.
Basic understanding of data handling and preprocessing techniques, which are essential for preparing data for machine learning models.
An analytical mindset and problem-solving skills to effectively convert business problems into machine learning problems and work through the stages of the ML pipeline.

These prerequisites are designed to ensure that participants are prepared to engage with the course material effectively and are able to take full advantage of the training program.

Target Audience for The Machine Learning Pipeline on AWS

The Machine Learning Pipeline on AWS course equips participants with practical AWS ML skills for real-world applications.

Data Scientists
Machine Learning Engineers
Data Engineers
Software Developers interested in machine learning
IT Professionals aiming to implement machine learning on AWS
AWS Cloud Practitioners
Cloud Solutions Architects
Technical Project Managers overseeing ML projects
Business Analysts looking to understand ML workflows
AI/ML Consultants
Research Scientists and Academics interested in deploying ML models
Data Analysts aspiring to transition to machine learning roles
Product Managers wanting to leverage machine learning in product development
Students and Educators in computer science and related fields

Learning Objectives - What you will Learn in this The Machine Learning Pipeline on AWS?

Introduction: The Machine Learning Pipeline on AWS course provides a comprehensive journey through the essentials of machine learning, leveraging Amazon SageMaker, and culminates in the deployment of ML models.
Learning Objectives and Outcomes:

Understand the fundamentals of machine learning, including its use cases, types, and key concepts.
Gain proficiency in the different stages of the ML pipeline and their significance in project implementation.
Become familiar with Amazon SageMaker and its integration with Jupyter notebooks for developing ML models.
Learn to formulate real-world problems into machine learning challenges and identify when ML is the appropriate solution.
Develop skills in data preprocessing, visualization, and utilization of Amazon SageMaker Ground Truth for data labeling.
Acquire the ability to select appropriate algorithms for model training and understand the role of loss functions and gradient descent.
Master the techniques for evaluating the performance of classification and regression models within the AWS environment.
Explore advanced concepts in feature engineering, including extraction, selection, and transformation, and practice hyperparameter tuning with Amazon SageMaker.
Learn to deploy, infer, and monitor ML models on Amazon SageMaker, including deploying ML at the edge.
Complete the course with the capability to build, train, tune, and deploy ML models on AWS, culminating in a final project presentation.

Course Fee	3,510
Exam Fee	300
Total Fees (without exam)	3,510 (USD)

Course Fee	2,700
Exam Fee	300
Total Fees (without exam)	2,700 (USD)

Flexi Video	16,449
Official E-coursebook
Exam Voucher (optional)
Hands-On-Labs²	4,159
+ GST 18%	4,259
Total Fees (without exam & Labs)	22,359 (INR)
Total Fees (with Labs)	28,359 (INR)

The Machine Learning Pipeline on AWS Course Overview

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Koenig's Unique Offerings

Course Prerequisites

Target Audience for The Machine Learning Pipeline on AWS

Learning Objectives - What you will Learn in this The Machine Learning Pipeline on AWS?

Suggested Courses

The Machine Learning Pipeline on AWS Course Overview

3,510 ♱

2,700 ♱

♱

Fee On Request

Filter By:

Request More Information

Koenig's Unique Offerings

Course Prerequisites

Target Audience for The Machine Learning Pipeline on AWS

Learning Objectives - What you will Learn in this The Machine Learning Pipeline on AWS?

Suggested Courses

3,510^♱

2,700^♱

^♱