The Machine Learning Pipeline on AWS
The course explores the usage of the iterative Machine Learning (ML) pipeline to solve real-world business problems in a project-based environment. You will learn about each phase of the pipeline from an experienced AWS instructor via live presentations and demonstrations. You will then go on to complete a project while solving one of the three business problems such as fraud detection, recommendation engines or flight delays. By the end of this course the students will have built, trained, evaluated and deployed a ML model using Amazon SageMaker to solve a selected business problem. The course also prepares you for the AWS Certified Machine Learning – Speciality certification.
This course is recommended for developers, solution architects, data engineers and anyone who wishes to learn more about the ML pipeline using Amazon SageMaker. We recommend that the learners have basic knowledge of Python programming language, basic understanding of AWS cloud services and basic experience of working in a Jupyter notebook environment
4 Days
Live Class
Certificate on completion
You will learn about the following
- 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
What experience you need
- 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
Who should take this course
- Developers
- Solutions Architects
- Data Engineers
- All learners keen to learn about the ML pipeline using Amazon SageMaker
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
Popular AWS Courses
FAQs
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 us at info@cloudwizardconsulting.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 . With regards to the maximum number, we can accomodate 30 learners in one batch.
- Training Delivered by an Amazon Authorized Instructor.
- AWS Content E-Kit
- Hands-on-labs for 30 days
- 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:Â
- 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
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
Talk to a Learning Advisor
Tablet View
Popular AWS Courses
FAQs
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 us at info@cloudwizardconsulting.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 . With regards to the maximum number, we can accomodate 30 learners in one batch.
- Training Delivered by an Amazon Authorized Instructor.
- AWS Content E-Kit
- Hands-on-labs for 30 days
- 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)
The Machine Learning Pipeline on AWS
The course explores the usage of the iterative Machine Learning (ML) pipeline to solve real-world business problems in a project-based environment. You will learn about each phase of the pipeline from an experienced AWS instructor via live presentations and demonstrations. You will then go on to complete a project while solving one of the three business problems such as fraud detection, recommendation engines or flight delays. By the end of this course the students will have built, trained, evaluated and deployed a ML model using Amazon SageMaker to solve a selected business problem. The course also prepares you for the AWS Certified Machine Learning – Speciality certification.
This course is recommended for developers, solution architects, data engineers and anyone who wishes to learn more about the ML pipeline using Amazon SageMaker. We recommend that the learners have basic knowledge of Python programming language, basic understanding of AWS cloud services and basic experience of working in a Jupyter notebook environment
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:Â
- 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
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
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).
