Amazon SageMaker Studio for Data Scientists

This course empowers data scientists to swiftly prepare, build, train, deploy, and monitor machine learning (ML) models. Seasoned data scientists can benefit from the course by gaining skills to master SageMaker Studio tools, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, enhancing productivity across the ML lifecycle. We recommend that all students complete the AWS Technical Essentials course before enrolling in this program. Additionally, those without prior experience in data science should complete The Machine Learning Pipeline on AWS and Deep Learning on AWS courses, followed by gaining 1-year on-the-job experience.


In this course, you will learn to:

  • Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
  • Use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
  • And much more

This course is intended for:

  • Experienced data scientists who are proficient in ML and deep learning fundamentals.
  • Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.

We recommend that all attendees of this course have:

  • 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

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


  • 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
Why choose Cloud Wizard
  • Advanced Tier Training Partner
  • Amazon Authorised Instructors
  • Official AWS Content
  • Hands-on Labs
Class Deliverables
  • E-Content kit by AWS
  • Hands-on labs
  • Class completion certificates
  • Exam Prep sessions

Dates Available - Click on Book Now to proceed

Classroom3 daysAll DayJune 26, 202445,000
Virtual3 daysAll DayJuly 8, 202445,000
Virtual3 daysAll DayJuly 22, 202445,000
Virtual3 daysAll DayAugust 5, 202445,000
Virtual3 daysAll DayAugust 19, 202445,000
Virtual3 daysAll DaySeptember 2, 202445,000
Virtual3 daysAll DaySeptember 16, 202445,000

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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.

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)





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