Amazon SageMaker Studio for Data Scientists

Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for
ML. This course prepares experienced data scientists to use the tools that are a part of SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve
productivity at every step of the ML lifecycle.

Learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor. The three-day, advanced level course helps experienced data scientists build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows to reduce training time from hours to minutes with optimized infrastructure. This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.

3 Days / 24 Hours

Live Class

Certificate on completion

45,000

Choose a date

You will learn about the following

  • Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using
    Amazon SageMaker Studio

What experience you need

  • Experience using ML frameworks
  • Python programming experience
  • At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
  • AWS Technical Essentials digital or classroom training

Who should take this course

  • Experienced data scientists who are proficient in ML and deep learning fundamentals

Module 1: Amazon SageMaker Studio Setup

  • JupyterLab Extensions in SageMaker Studio
  • Demonstration: SageMaker user interface demo

Module 2: Data Processing

  • Using SageMaker Data Wrangler for data processing
  • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
  • Using Amazon EMR
  • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
  • Using AWS Glue interactive sessions
  • Using SageMaker Processing with custom scripts
  • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
  • SageMaker Feature Store
  • Hands-On Lab: Feature engineering using SageMaker Feature Store

Module 3: Model Development

  • SageMaker training jobs
  • Built-in algorithms
  • Bring your own script
  • Bring your own container
  • SageMaker Experiments
  • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models

Module 4: Model Development (continued)

  • SageMaker Debugger
  • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
  • Automatic model tuning
  • SageMaker Autopilot: Automated ML
  • Demonstration: SageMaker Autopilot
  • Bias detection
  • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
  • SageMaker Jumpstart

Module 5: Deployment and Inference

  • SageMaker Model Registry
  • SageMaker Pipelines
  • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
  • SageMaker model inference options
  • Scaling
  • Testing strategies, performance, and optimization
  • Hands-On Lab: Inferencing with SageMaker Studio

Module 6: Monitoring

  • Amazon SageMaker Model Monitor
  • Discussion: Case study
  • Demonstration: Model Monitoring

Module 7: Managing SageMaker Studio Resources and Updates

  • Accrued cost and shutting down
  • Updates

Capstone

  • Environment setup
  • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
  • Challenge 2: Create feature groups in SageMaker Feature Store
  • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
  • (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization
  • Challenge 5: Evaluate the model for bias using SageMaker Clarify
  • Challenge 6: Perform batch predictions using model endpoint
  • (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

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.

  1. Training Delivered by an Amazon Authorized Instructor.
  2. AWS Content E-Kit
  3. Hands-on-labs for 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

Amazon SageMaker Studio for Data Scientists

3 Days / 24 Hours

Live Class

Certificate on completion

45,000

(Taxes Extra)

Choose a date

Here’s what you’ll learn in this Amazon SageMaker Studio for Data Scientists course:

  • Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using
    Amazon SageMaker Studio

Here are the recommended pre-requisites

  • Experience using ML frameworks
  • Python programming experience
  • At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
  • AWS Technical Essentials digital or classroom training

You can enroll in Amazon SageMaker Studio for Data Scientists Course if you are an

  • Experienced data scientists who are proficient in ML and deep learning fundamentals

Module 1: Amazon SageMaker Studio Setup

  • JupyterLab Extensions in SageMaker Studio
  • Demonstration: SageMaker user interface demo

Module 2: Data Processing

  • Using SageMaker Data Wrangler for data processing
  • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
  • Using Amazon EMR
  • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
  • Using AWS Glue interactive sessions
  • Using SageMaker Processing with custom scripts
  • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
  • SageMaker Feature Store
  • Hands-On Lab: Feature engineering using SageMaker Feature Store

Module 3: Model Development

  • SageMaker training jobs
  • Built-in algorithms
  • Bring your own script
  • Bring your own container
  • SageMaker Experiments
  • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models

Module 4: Model Development (continued)

  • SageMaker Debugger
  • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
  • Automatic model tuning
  • SageMaker Autopilot: Automated ML
  • Demonstration: SageMaker Autopilot
  • Bias detection
  • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
  • SageMaker Jumpstart

Module 5: Deployment and Inference

  • SageMaker Model Registry
  • SageMaker Pipelines
  • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
  • SageMaker model inference options
  • Scaling
  • Testing strategies, performance, and optimization
  • Hands-On Lab: Inferencing with SageMaker Studio

Module 6: Monitoring

  • Amazon SageMaker Model Monitor
  • Discussion: Case study
  • Demonstration: Model Monitoring

Module 7: Managing SageMaker Studio Resources and Updates

  • Accrued cost and shutting down
  • Updates

Capstone

  • Environment setup
  • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
  • Challenge 2: Create feature groups in SageMaker Feature Store
  • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
  • (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization
  • Challenge 5: Evaluate the model for bias using SageMaker Clarify
  • Challenge 6: Perform batch predictions using model endpoint
  • (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

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.

  1. Training Delivered by an Amazon Authorized Instructor.
  2. AWS Content E-Kit
  3. Hands-on-labs for 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)

Amazon SageMaker Studio for Data Scientists

Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for
ML. This course prepares experienced data scientists to use the tools that are a part of SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve
productivity at every step of the ML lifecycle.

Learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor. The three-day, advanced level course helps experienced data scientists build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows to reduce training time from hours to minutes with optimized infrastructure. This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.

3 Days / 24 Hours

Live Class

Certificate on completion

45,000

Choose a date

Objectives

Here’s what you’ll learn in this Amazon SageMaker Studio for Data Scientists course:

  • Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using
    Amazon SageMaker Studio

Prerequisites

Here are the recommended pre-requisites

  • Experience using ML frameworks
  • Python programming experience
  • At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
  • AWS Technical Essentials digital or classroom training

Intended Audience

You can enroll in Amazon SageMaker Studio for Data Scientists Course if you are an

  • Experienced data scientists who are proficient in ML and deep learning fundamentals

Module 1: Amazon SageMaker Studio Setup

  • JupyterLab Extensions in SageMaker Studio
  • Demonstration: SageMaker user interface demo

Module 2: Data Processing

  • Using SageMaker Data Wrangler for data processing
  • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
  • Using Amazon EMR
  • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
  • Using AWS Glue interactive sessions
  • Using SageMaker Processing with custom scripts
  • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
  • SageMaker Feature Store
  • Hands-On Lab: Feature engineering using SageMaker Feature Store

Module 3: Model Development

  • SageMaker training jobs
  • Built-in algorithms
  • Bring your own script
  • Bring your own container
  • SageMaker Experiments
  • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models

Module 4: Model Development (continued)

  • SageMaker Debugger
  • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
  • Automatic model tuning
  • SageMaker Autopilot: Automated ML
  • Demonstration: SageMaker Autopilot
  • Bias detection
  • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
  • SageMaker Jumpstart

Module 5: Deployment and Inference

  • SageMaker Model Registry
  • SageMaker Pipelines
  • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
  • SageMaker model inference options
  • Scaling
  • Testing strategies, performance, and optimization
  • Hands-On Lab: Inferencing with SageMaker Studio

Module 6: Monitoring

  • Amazon SageMaker Model Monitor
  • Discussion: Case study
  • Demonstration: Model Monitoring

Module 7: Managing SageMaker Studio Resources and Updates

  • Accrued cost and shutting down
  • Updates

Capstone

  • Environment setup
  • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
  • Challenge 2: Create feature groups in SageMaker Feature Store
  • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
  • (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization
  • Challenge 5: Evaluate the model for bias using SageMaker Clarify
  • Challenge 6: Perform batch predictions using model endpoint
  • (Optional) Challenge 7: Automate full model development process using SageMaker Pipeline

Talk to a Learning Advisor

Please enable JavaScript in your browser to complete this form.

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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