The Machine Learning Pipeline on AWS
Individuals will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
The learners will explore how to use the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment.
This course also helps you prepare for the AWS Certified Machine Learning Exam
This program is intended for Developers, Solutions Architects, Data Engineers, Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker.
4 Days
Live Class
Certificate on completion
Objectives
In this Machine Learning Pipleline on AWS course, you will learn to:
- Use the Machine Learning pipeline to address business issues
- Using Amazon SageMaker you can develop, test, deploy, and fine-tune an ML model.
- Learn a few recommended practices for creating scalable, economical, and secure Machine Learning Pipelines in AWS.
- After completing the course, apply Machine learning (ML) to a genuine business issue.
- Prepare for the AWS Machine Learning Certification
Prerequisites
Attendees of this Machine Learning course are advised to have the following:
- Some familiarity with the Python programming language
- A fundamental knowledge of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Experience in working with Jupyter notebooks on a entry level
Intended Audience
The following group of individuals are likely to benefit
- Developers
- Solutions Architects
- Data Engineers
- All learners keen to learn about the ML pipeline using Amazon SageMaker
Activities
In this course, you will:
- The course will emphasize a practical learning environment, including group presentations, demonstrations and hands-on labs, to enhance a basic understanding of how AWS machine learning works.
Module 0: Introduction
- Pre-assessment
Module 1: 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
Module 2: Introduction to Amazon SageMaker
- Introduction to Amazon SageMaker
- Demo: Amazon SageMaker and Jupyter notebooks
- Hands-on: Amazon SageMaker and Jupyter notebooks
Module 3: 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
- Practice problem formulation
- Formulate problems for projects
Checkpoint 1 and Answer Review
Module 4: Preprocessing
- Overview of data collection and integration, and techniques for data preprocessing and visualization
- Practice preprocessing
- Preprocess project data
- Class discussion about projects
Checkpoint 2 and Answer Review
Module 5: 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
Module 6: Model Evaluation
- How to evaluate classification models
- How to evaluate regression models
- Practice model training and evaluation
- Train and evaluate project models
- Initial project presentations
Checkpoint 3 and Answer Review
Module 7: Feature Engineering and Model Tuning
- Feature extraction, selection, creation, and transformation
- Hyperparameter tuning
- Demo: SageMaker hyperparameter optimization
- Practice feature engineering and model tuning
- Apply feature engineering and model tuning to projects
- Final project presentations
Module 8: Deployment
- How to deploy, inference, and monitor your model on Amazon SageMaker
- Deploying ML at the edge
- Demo: Creating an Amazon SageMaker endpoint
- Post-assessment
- Course wrap-up
Talk to a Learning Advisor
Exam Readiness
AWS Certified Machine Learning - Speciality
Certification
AWS Certified Machine Learning - Specialty
FAQs
Yes, we are an AWS Advanced Tier Training Partner
Anyone who wants to start a profession in AWS cloud is fit to enroll in this course. No prior knowledge of coding or other technical skills is required.
To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.
You may reach out at the contact number listed on our official website or write to us at info@cloudwizard.wpenginepowered.com.
Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order
There is no minimum number of candidates required, we are happy to train 1 to 1 should you wish. With regard to the maximum number, we can accommodate 30 learners in one batch.
1. Training delivered by an Amazon Authorised Instructor
2. AWS Content E-Kit
3. Hands-on labs- 30 days
4. Class attendance certificate
You will get the access to course content & lab on first day of your training session.
The course completion certificate will be issued to your email id within 2 weeks of completing your course.
A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day).
MOBILE LAYOUT
The Machine Learning Pipeline on AWS
4 Days
Live Class
Certificate on completion
Objectives
In this Machine Learning Pipleline on AWS course, you will learn to:
- Use the Machine Learning pipeline to address business issues
- Using Amazon SageMaker you can develop, test, deploy, and fine-tune an ML model.
- Learn a few recommended practices for creating scalable, economical, and secure Machine Learning Pipelines in AWS.
- After completing the course, apply Machine learning (ML) to a genuine business issue.
- Prepare for the AWS Machine Learning Certification
Prerequisites
Attendees of this Machine Learning course are advised to have the following:
- Some familiarity with the Python programming language
- A fundamental knowledge of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Experience in working with Jupyter notebooks on a entry level
Intendend Audience
The following group of individuals are likely to benefit
- Developers
- Solutions Architects
- Data Engineers
- All learners keen to learn about the ML pipeline using Amazon SageMaker
Activities
In this course, you will:
- The course will emphasize a practical learning environment, including group presentations, demonstrations and hands-on labs, to enhance a basic understanding of how AWS machine learning works.
Module Breakdown
Module 0: Introduction
- Pre-assessment
Module 1: 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
Module 2: Introduction to Amazon SageMaker
- Introduction to Amazon SageMaker
- Demo: Amazon SageMaker and Jupyter notebooks
- Hands-on: Amazon SageMaker and Jupyter notebooks
Module 3: 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
- Practice problem formulation
- Formulate problems for projects
Checkpoint 1 and Answer Review
Module 4: Preprocessing
- Overview of data collection and integration, and techniques for data preprocessing and visualization
- Practice preprocessing
- Preprocess project data
- Class discussion about projects
Checkpoint 2 and Answer Review
Module 5: 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
Module 6: Model Evaluation
- How to evaluate classification models
- How to evaluate regression models
- Practice model training and evaluation
- Train and evaluate project models
- Initial project presentations
Checkpoint 3 and Answer Review
Module 7: Feature Engineering and Model Tuning
- Feature extraction, selection, creation, and transformation
- Hyperparameter tuning
- Demo: SageMaker hyperparameter optimization
- Practice feature engineering and model tuning
- Apply feature engineering and model tuning to projects
- Final project presentations
Module 8: Deployment
- How to deploy, inference, and monitor your model on Amazon SageMaker
- Deploying ML at the edge
- Demo: Creating an Amazon SageMaker endpoint
- Post-assessment
- Course wrap-up
Exam Readiness
Certifications
AWS Certified Machine Learning - Specialty
Talk to a Learning Advisor
Tablet View
The Machine Learning Pipeline on AWS
Individuals will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
The learners will explore how to use the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment.
This course also helps you prepare for the AWS Certified Machine Learning Exam
This program is intended for Developers, Solutions Architects, Data Engineers, Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker.
4 Days
Live Class
Certificate on completion
Objectives
In this Machine Learning Pipleline on AWS course, you will learn to:
- Use the Machine Learning pipeline to address business issues
- Using Amazon SageMaker you can develop, test, deploy, and fine-tune an ML model.
- Learn a few recommended practices for creating scalable, economical, and secure Machine Learning Pipelines in AWS.
- After completing the course, apply Machine learning (ML) to a genuine business issue.
- Prepare for the AWS Machine Learning Certification
Prerequisites
Attendees of this Machine Learning course are advised to have the following:
- Some familiarity with the Python programming language
- A fundamental knowledge of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Experience in working with Jupyter notebooks on a entry level
Intended Audience
The following group of individuals are likely to benefit
- Developers
- Solutions Architects
- Data Engineers
- All learners keen to learn about the ML pipeline using Amazon SageMaker
Activities
In this course, you will:
- The course will emphasize a practical learning environment, including group presentations, demonstrations and hands-on labs, to enhance a basic understanding of how AWS machine learning works.
Module 0: Introduction
- Pre-assessment
Module 1: 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
Module 2: Introduction to Amazon SageMaker
- Introduction to Amazon SageMaker
- Demo: Amazon SageMaker and Jupyter notebooks
- Hands-on: Amazon SageMaker and Jupyter notebooks
Module 3: 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
- Practice problem formulation
- Formulate problems for projects
Checkpoint 1 and Answer Review
Module 4: Preprocessing
- Overview of data collection and integration, and techniques for data preprocessing and visualization
- Practice preprocessing
- Preprocess project data
- Class discussion about projects
Checkpoint 2 and Answer Review
Module 5: 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
Module 6: Model Evaluation
- How to evaluate classification models
- How to evaluate regression models
- Practice model training and evaluation
- Train and evaluate project models
- Initial project presentations
Checkpoint 3 and Answer Review
Module 7: Feature Engineering and Model Tuning
- Feature extraction, selection, creation, and transformation
- Hyperparameter tuning
- Demo: SageMaker hyperparameter optimization
- Practice feature engineering and model tuning
- Apply feature engineering and model tuning to projects
- Final project presentations
Module 8: Deployment
- How to deploy, inference, and monitor your model on Amazon SageMaker
- Deploying ML at the edge
- Demo: Creating an Amazon SageMaker endpoint
- Post-assessment
- Course wrap-up
Talk to a Learning Advisor
Exam Readiness
AWS Certified Machine Learning - Speciality
Certification
AWS Certified Machine Learning - Specialty
FAQs
Yes, we are an AWS Advanced Tier Training Partner
Anyone who wants to start a profession in AWS cloud is fit to enroll in this course. No prior knowledge of coding or other technical skills is required.
To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.
You may reach out at the contact number listed on our official website or write to us at info@cloudwizard.wpenginepowered.com.
Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order
There is no minimum number of candidates required, we are happy to train 1 to 1 should you wish. With regard to the maximum number, we can accommodate 30 learners in one batch.
1. Training delivered by an Amazon Authorised Instructor
2. AWS Content E-Kit
3. Hands-on labs- 30 days
4. Class attendance certificate
You will get the access to course content & lab on first day of your training session.
The course completion certificate will be issued to your email id within 2 weeks of completing your course.
A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day).
