The Machine Learning Pipeline on AWS Training

Course Description

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.

Who should attend this 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.

What you will learn

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.

With Microtek Learning, you’ll receive:

Certified Instructor-led training
Industry Best Trainers
Official Training Course Student Handbook
Pre and Post assessments/evaluations
Collaboration with classmates (not available for a self-paced course)
Real-world knowledge activities and scenarios
Exam scheduling support*
Learn and earn program*
Practice Tests
Knowledge acquisition and exam-oriented
Interactive online course.
Support from an approved expert
For Government and Private pricing*

Our Clients

For many years, Microtek Learning has been helping organizations, leaders, and professionals to reach their maximum performance by addressing the challenges they are facing.

300+ enterprise clients
100,000+ professionals trained
Service 70 of the Fortune 100
96% of our clients would recommend us

Our Awards

Curriculum

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

Introduction to Amazon SageMaker

Introduction to Amazon SageMaker

Demo: Amazon SageMaker and Jupyter notebooks

Lab: Introduction to Amazon SageMaker

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

Preprocessing

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

Lab: Data Preprocessing (including project work)

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

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

Feature Engineering and Model Tuning

Feature extraction, selection, creation, and transformation

Hyperparameter tuning

Demo: SageMaker hyperparameter optimization

Lab: Feature Engineering (including project work)

Module Deployment

How to deploy, inference, and monitor your model on Amazon SageMaker

Deploying ML at the edge

Course Wrap-Up

Project Share-Out 2

Post-Assessment

Wrap-up

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