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

45,000

Choose a date

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

Please enable JavaScript in your browser to complete this form.

Exam Readiness

AWS Certified Machine Learning - Speciality

Your ability to create, implement, deploy, and maintain Machine Learning (ML) solutions for specific business issues is confirmed by passing the AWS Certified Machine Learning – Specialty exam. Explore the exam’s topic areas, such as data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, in this half-day advanced-level course. The course teaches you how to apply the topics being examined so you may more readily eliminate incorrect solutions. It also discusses how to understand exam questions in each academic area.

Certification

AWS Certified Machine Learning - Specialty

This certification allows businesses to hire employees with essential requirements of carrying out some critical cloud activities on AWS cloud. AWS Certified Machine Learning – Specialty status attests to one’s proficiency in creating, honing, optimizing, and deploying machine learning (ML) models on the platform.

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

45,000

(Taxes Extra)

Choose a date

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

Your ability to create, implement, deploy, and maintain Machine Learning (ML) solutions for specific business issues is confirmed by passing the AWS Certified Machine Learning – Specialty exam. Explore the exam’s topic areas, such as data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, in this half-day advanced-level course. The course teaches you how to apply the topics being examined so you may more readily eliminate incorrect solutions. It also discusses how to understand exam questions in each academic area.

Certifications

AWS Certified Machine Learning - Specialty

This certification allows businesses to hire employees with essential requirements of carrying out some critical cloud activities on AWS cloud. AWS Certified Machine Learning – Specialty status attests to one’s proficiency in creating, honing, optimizing, and deploying machine learning (ML) models on the platform.

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

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

45,000

Choose a date

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

Please enable JavaScript in your browser to complete this form.

Exam Readiness

AWS Certified Machine Learning - Speciality

Your ability to create, implement, deploy, and maintain Machine Learning (ML) solutions for specific business issues is confirmed by passing the AWS Certified Machine Learning – Specialty exam. Explore the exam’s topic areas, such as data engineering, exploratory data analysis, modeling, and machine learning implementation and operations, in this half-day advanced-level course. The course teaches you how to apply the topics being examined so you may more readily eliminate incorrect solutions. It also discusses how to understand exam questions in each academic area.

Certification

AWS Certified Machine Learning - Specialty

This certification allows businesses to hire employees with essential requirements of carrying out some critical cloud activities on AWS cloud. AWS Certified Machine Learning – Specialty status attests to one’s proficiency in creating, honing, optimizing, and deploying machine learning (ML) models on the platform.

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).

AWS Bundles – Active Directory Bundle

  • Simplify your Active Directory integration with our pre-packaged AWS resources.
  • It’s a combination package of EC2 pre-installed with Microsoft Active Directory and Domain Controllers.
  • You can either choose to install the Active Directory alone or add the Domain Controller together. The Roles and Services are configured and installed.
  • It is a One-size fits all bundle for most SMB spaces when it comes to Active Directory Deployment.
  • In terms of security, the VM is pre-configured with host-based firewall to allow RDP.
  • When to use this bundle?
  • A new Active Directory Setup in AWS
  • Extending your AD infrastructure into AWS
  • Adding additional Domain Controllers in AD
  • Price starting from USD57/month

Bundle Overview

  • 1 x Virtual Machine (Windows/Ubuntu)
  • You can choose from 3 database server sizes: (Small: 2vCPU’s, 4GB RAM, 50GB / Medium: 2vCPU’s, 8GB RAM, 50GB / Large: 4vCPU’s, 16GB RAM, 50GB.)
  • Pre-configured host-based firewall to allow RDP
  • Deployable in existing networks in AWS or new networks (Network Bundle)
  • ADDS roles and services configured and installed
  • Automatically joined to your AD Domain

AWS Bundles – Web Hosting Bundle

  • Quickly deploy and manage your web applications with our pre-packaged AWS resources.
  • This bundle is designed for new customers just starting their journey on AWS and customers migrating a Windows-based web application to AWS. It is suitable for any workloads that require an application server and MySQL database server.
  • The bundle is pre-configured with subnets, web server and a database in Multi-AZ environment with routing and preconfigured host firewalls.
  • Price starting from USD1,666/month

Bundle Overview

  • 2 x Public Subnet
  • 2 x Private Subnets with no exposure to the internet
  • 1 x Windows WebApp Server
  • 1 x RDS MS SQL
  • 3 available sizes:
    • Small, 2 vCPUs, 4GB RAM,40GB EBS, 2 vCPUs, 8GB RAM, 40GB EBS 
    • Medium, 2 vCPUs, 8GB RAM,80GB EBS, 4 vCPUs, 16GB RAM,80GB EBS 
    • Large, 4 vCPUs, 16GB RAM,120GB EBS, 8 vCPUs, 32GB RAM,120GB EBS
  • Multi-AZ Deployment
  • Preconfigured routing and security group for web server and database

AWS Bundles – Secure Storage Bundle

  • Safeguard your data with our secure storage solution.
  • This One-size-fits all bundle enables you and your customer to build a secured storage environment in AWS.
  • Your AWS S3 will be added with additional securities encompassing storage security such as data loss prevention, scanning malware or malicious files uploaded or backed up in your Cloud storage.
  • It employs AWS’s native security features plus the Cloud market’s best security solutions to make the storage impregnable.
  • When to use this bundle …
    • New Customers Migrating to AWS
    • Storage Offload from on-premise to AWS
    • Application Modernisation

  • Price starting from USD25/month

Bundle Overview

  • 1 x S3 Bucket
  • You can choose from 3 sizes: (Small: 50GB / Medium: 100GB / Large: 150GB)
  • Deployable in existing networks in AWS or new networks (Network Bundle)
  •  

AWS Bundles – AWS WAF Bundle

  • Protect your applications with our pre-packaged AWS WAF solution.
  • This bundle is designed for…
    • A new customer just starting its journey on AWS and is building a web application
    • A customer is migrating a web application to AWS
    • A customer has a workload on AWS (ELB/API Gateway/CloudFront) that they want to secure from web-based attacks.
  • It protects against common application attacks such as XSS attacks, Bots and DDoS, SQL injection, and unwanted malicious traffics. Workloads such as ELB, API Gateway, and CloudFront will be secured with one-click bundle installation.
  • Price starting from USD5/month

Bundle Overview

  • Protection against common application vulnerabilities or other unwanted traffic
  • Protection from malicious traffic that increases resource consumption
  • Protection from XSS attacks.
  • (Optional) Protection from bots and DDoS attacks.
  • (Optional) Protection from SQL Injection attacks
  • Protection from PHP/WordPress attacks
  • Centralised WAF logging and monitoring

AWS Bundle – Network Bundle

  • Set up your network infrastructure easily and securely with our pre-packaged AWS resources.
  • This bundle is designed for the new customer just starting their journey on AWS2 and any new workloads that require their own network.
  • Bolster Your Security Posture
  • The VM is a pre-configured Routing and host-based firewall to allow RDP. The bundle is also pre-configured with host firewalls for web servers and databases.
  • Price starting from USD135/month

Bundle Overview

  • 2 x Public subnets in 2 Data Centers
  • 2 x Private Subnets behind a Router with NAT Capabilities
  • 2 x Private Subnets with no exposure to the internet
  • 1 x Bastion Subnet
  • 1 x Bastion Host/Jump Server
  • Preconfigured Routing
  • Preconfigured host firewalls for web servers and databases
  • Architectural Best Practices included

AWS Bundles – Database Bundle

  • Deploy and easily manage your databases using our pre-packaged AWS resources.
  • This bundle installs the managed database of your choice (MySQL, MSSQL, PostresSQL, Maria) and is easy to deploy on existing networks or new networks
  • Standardised way to deploy databases in AWS
  • The VM is pre-configured with host-based firewall to allow a secured connection to the database.
  • Price starting from USD200/month
 

Bundle Overview

  • 1 x Managed Database Server (MySQL, MSSQL, PostgreSQL, Maria)
  • You can choose from 3 database server sizes: (Small: 2vCPU’s, 4GB RAM, 50GB / Medium: 2vCPU’s, 8GB RAM, 50GB / Large: 2vCPU’s, 16GB RAM, 50GB
  • Pre-configured host based firewall to allow connection on the database
  • Deployable in existing networks in AWS or new networks (Network Bundle)

AWS Bundles – Virtual Machines Bundle

  • Quickly deploy virtual machines with our easy-to-use automation templates for your computing needs
  • Virtual Machines bundle deploys either Windows or Ubuntu Virtual Machines in AWS environment with sizes of your choice.
  • This is a barebone Virtual machine, and the server placement depends on your need – it can be in a Public subnet if you want your server exposed to the internet or a Private subnet if you are using VMs that do not require internet exposure, such as databases.
  • Price starting from USD50/month

Bundle Overview

  • 1 x Virtual Machine (Windows/Ubuntu)
  • You can choose from 3 sizes: (Small: 2vCPU’s, 4GB RAM, 50GB / Medium: 2vCPU’s, 8GB RAM, 50GB / Large: 2vCPU’s, 16GB RAM, 50GB)
  • Pre-configured host based firewall to allow RDP
  • Deployable in existing networks in AWS or new networks (Network Bundle)