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Practical Data Science with Amazon SageMaker

Artificial Intelligence and Machine learning are becoming very mainstream technologies for businesses, hence it becomes very important to understand how to work with data scientists and develop applications that integrate with Machine Learning (ML). The learner will learn how data scientists are developing solutions on the AWS Cloud with Amazon SageMaker. You will get to learn how to develop, train and deploy ML models working with an Amazon Authorized Instructor through demonstrations and hands-on labs

This course is intended for DevOps Engineers and Application developers who are keen to learn to develop applications that work well with Machine Learning. Entry level knowledge of Python programing and basic knowledge of statistics will be helpful

The class is delivered with presentations, hands-on labs and demonstrations

1 Day / 8 Hours

Live Class

Certificate on completion

15,000

Choose a date

You will learn about the following

  • Assemble a training dataset
  • Develop and assess a machine learning model
  • Automate the fine-tuning of a machine learning model
  • Create a machine learning model that can be used in production.
  • Examine the output of machine learning models critically.

What experience you need

  • Python language
  • Essential machine learning (ML)

Who should take this course

  • Data Scientists
  • Developers

Activities

  • Live Presentations
  • Hands-On Labs
  • Group Exercises

Module 1: Introduction to machine learning

  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline

Module 2: Introduction to data prep and SageMaker

  • Training and test dataset defined
  • Introduction to SageMaker
  • Demonstration: SageMaker console
  • Demonstration: Launching a Jupyter notebook

Module 3: Problem formulation and dataset preparation

  • Business challenge: Customer churn
  • Review customer churn dataset

Module 4: Data analysis and visualization

  • Demonstration: Loading and visualizing your dataset
  • Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demonstration: Cleaning the data

Module 5: Training and evaluating a model

  • Types of algorithms
  • XGBoost and SageMaker
  • Demonstration: Training the data
  • Exercise 3: Finishing the estimator definition
  • Exercise 4: Setting hyper parameters
  • Exercise 5: Deploying the model
  • Demonstration: hyper parameter tuning with SageMaker
  • Demonstration: Evaluating model performance

Module 6: Automatically tune a model

  • Automatic hyper parameter tuning with SageMaker
  • Exercises 6-9: Tuning jobs

Module 7: Deployment / production readiness

  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling
  • Demonstration: Configure and test auto scaling
  • Demonstration: Check hyper parameter tuning job
  • Demonstration: AWS Auto Scaling
  • Exercise 10-11: Set up AWS Auto Scaling

Module 8: Relative cost of errors

  • Cost of various error types
  • Demo: Binary classification cutoff

Module 9: Amazon SageMaker architecture and features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

Exam Readiness

Exam Voucher

Certification

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

Practical Data Science with Amazon SageMaker

1 Day / 8 Hours

Live Class

Certificate on completion

15,000

(Taxes Extra)

Choose a date

You’ll be covering the following things in this course:

  • Assemble a training dataset
  • Develop and assess a machine learning model
  • Automate the fine-tuning of a machine learning model
  • Create a machine learning model that can be used in production.
  • Examine the output of machine learning models critically.

You’ll be required to have familiarity with the following:

  • Python language
  • Essential machine learning (ML)

The Practical Data Science With Amazon SageMaker course is fit for you if you belong to either of the following group of individuals:

  • Data Scientists
  • Developers

This course will include the following:

  • Live Presentations
  • Hands-On Labs
  • Group Exercises

Module 1: Introduction to machine learning

  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline

Module 2: Introduction to data prep and SageMaker

  • Training and test dataset defined
  • Introduction to SageMaker
  • Demonstration: SageMaker console
  • Demonstration: Launching a Jupyter notebook

Module 3: Problem formulation and dataset preparation

  • Business challenge: Customer churn
  • Review customer churn dataset

Module 4: Data analysis and visualization

  • Demonstration: Loading and visualizing your dataset
  • Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demonstration: Cleaning the data

Module 5: Training and evaluating a model

  • Types of algorithms
  • XGBoost and SageMaker
  • Demonstration: Training the data
  • Exercise 3: Finishing the estimator definition
  • Exercise 4: Setting hyper parameters
  • Exercise 5: Deploying the model
  • Demonstration: hyper parameter tuning with SageMaker
  • Demonstration: Evaluating model performance

Module 6: Automatically tune a model

  • Automatic hyper parameter tuning with SageMaker
  • Exercises 6-9: Tuning jobs

Module 7: Deployment / production readiness

  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling
  • Demonstration: Configure and test auto scaling
  • Demonstration: Check hyper parameter tuning job
  • Demonstration: AWS Auto Scaling
  • Exercise 10-11: Set up AWS Auto Scaling

Module 8: Relative cost of errors

  • Cost of various error types
  • Demo: Binary classification cutoff

Module 9: Amazon SageMaker architecture and features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

Exam Readiness

Certifications

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

Tablet View

Practical Data Science with Amazon SageMaker

Artificial Intelligence and Machine learning are becoming very mainstream technologies for businesses, hence it becomes very important to understand how to work with data scientists and develop applications that integrate with Machine Learning (ML). The learner will learn how data scientists are developing solutions on the AWS Cloud with Amazon SageMaker. You will get to learn how to develop, train and deploy ML models working with an Amazon Authorized Instructor through demonstrations and hands-on labs

This course is intended for DevOps Engineers and Application developers who are keen to learn to develop applications that work well with Machine Learning. Entry level knowledge of Python programing and basic knowledge of statistics will be helpful

The class is delivered with presentations, hands-on labs and demonstrations

1 Day / 8 Hours

Live Class

Certificate on completion

15,000

Choose a date

Objectives

You’ll be covering the following things in this course:

  • Assemble a training dataset
  • Develop and assess a machine learning model
  • Automate the fine-tuning of a machine learning model
  • Create a machine learning model that can be used in production.
  • Examine the output of machine learning models critically.

Prerequisites

You’ll be required to have familiarity with the following:

  • Python language
  • Essential machine learning (ML)

Intended Audience

The Practical Data Science With Amazon SageMaker course is fit for you if you belong to either of the following group of individuals:

  • Data Scientists
  • Developers

Activities

This course will include the following:

  • Live Presentations
  • Hands-On Labs
  • Group Exercises

Module 1: Introduction to machine learning

  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline

Module 2: Introduction to data prep and SageMaker

  • Training and test dataset defined
  • Introduction to SageMaker
  • Demonstration: SageMaker console
  • Demonstration: Launching a Jupyter notebook

Module 3: Problem formulation and dataset preparation

  • Business challenge: Customer churn
  • Review customer churn dataset

Module 4: Data analysis and visualization

  • Demonstration: Loading and visualizing your dataset
  • Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demonstration: Cleaning the data

Module 5: Training and evaluating a model

  • Types of algorithms
  • XGBoost and SageMaker
  • Demonstration: Training the data
  • Exercise 3: Finishing the estimator definition
  • Exercise 4: Setting hyper parameters
  • Exercise 5: Deploying the model
  • Demonstration: hyper parameter tuning with SageMaker
  • Demonstration: Evaluating model performance

Module 6: Automatically tune a model

  • Automatic hyper parameter tuning with SageMaker
  • Exercises 6-9: Tuning jobs

Module 7: Deployment / production readiness

  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling
  • Demonstration: Configure and test auto scaling
  • Demonstration: Check hyper parameter tuning job
  • Demonstration: AWS Auto Scaling
  • Exercise 10-11: Set up AWS Auto Scaling

Module 8: Relative cost of errors

  • Cost of various error types
  • Demo: Binary classification cutoff

Module 9: Amazon SageMaker architecture and features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

Exam Readiness

Exam Voucher

Certification

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).

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