Official AWS Course

Amazon SageMaker Studio for Data Scientists Training

Master the end-to-end ML lifecycle in Amazon SageMaker Studio, from data preparation and bias detection to pipelines, deployment, and drift monitoring. 3-day advanced course with 10 labs and a capstone.

👥 1893 Enrolled ⏱️ 3 Days 💼 Advanced Level ⭐ 4.9 ( 281 ) Reviews

500+Courses

10+ YrsExperience

95K+Global Learners

Live Online (Public Batch)

Instructor-led group class, fixed schedule

USD 1799

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Course Overview

Data scientists lose an enormous amount of time to things that are not data science: provisioning infrastructure, wiring together tools that do not quite fit, waiting on training jobs, and rebuilding the same preparation pipeline for the third time. Amazon SageMaker Studio exists to remove that overhead, and this course teaches experienced practitioners to use it properly across the whole machine learning lifecycle.

Across three days you will work through the full workflow. It starts with setup and navigation, then moves into data processing: collecting, cleaning, visualizing, and transforming data, building a repeatable pipeline, validating that data is genuinely ML-ready, and detecting bias in collected data while estimating baseline model accuracy. You will work hands-on with Data Wrangler, Amazon EMR for scale, SageMaker Processing with the Python SDK, and Feature Store.

Model development follows, covering training and tuning against business objectives with fairness and explainability in mind, automatic hyperparameter optimization, and using Debugger to surface problems while they are still cheap to fix. Labs cover Experiments for tracking iterations, Debugger alerts, and Clarify for bias and explainability. From there you move to deployment and inference, designing solutions that match real inference requirements, automating end-to-end workflows with SageMaker Pipelines, and managing versions and approval status through Model Registry.

The last day covers what happens after deployment: configuring Model Monitor to detect and alert on drift in data quality, model quality, bias, and feature attribution, plus the unglamorous but expensive topic of resource management, which instances accrue charges and when to shut things down. The course ends with a capstone where you prepare, build, train, and deploy a model on a tabular dataset you have not seen, with basic, intermediate, and advanced instruction paths depending on how much support you want.

Mode of Training

🏫 Classroom 💻 Live Online 🧪 Blended 👨‍👩‍👧‍👦 Private Group

Upcoming Schedules

Start Date	Time	Duration	Mode	Price
Sep 02, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Sep 16, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Sep 30, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Oct 14, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR
Oct 28, 2026	9:00 am - 5:00 pm	3 Days	online	$1799	GTR

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What you will learn

Set up SageMaker Studio and navigate its interface and tooling
Collect, clean, visualize, analyze, and transform data within SageMaker Studio
Build a repeatable data processing pipeline and validate that data is ML-ready
Detect bias in collected data and estimate baseline model accuracy
Develop, tune, and evaluate models against business objectives and fairness and explainability standards
Fine-tune models using automatic hyperparameter optimization
Use Debugger to surface issues during model development
Design and implement deployment solutions matched to inference requirements
Create, automate, and manage end-to-end workflows with SageMaker Pipelines
Register, version, approve, and deploy models through Model Registry
Configure Model Monitor to detect and alert on data quality, model quality, bias, and feature attribution drift
Manage SageMaker Studio resources and control costs

Who Should Attend This Course?

Experienced data scientists who are proficient in machine learning and deep learning fundamentals. Relevant background includes working with ML frameworks, Python programming, and the practical process of building, training, tuning, and deploying models.

The course also suits machine learning engineers and AI developers who already work with models and want to consolidate their workflow inside SageMaker Studio.

Prerequisites

At least one year of experience as a data scientist responsible for training, tuning, and deploying models
AWS Technical Essentials, or equivalent working knowledge of AWS
Proficiency in Python and familiarity with common ML frameworks

If you are not yet an experienced data scientist, AWS recommends building that foundation first through The Machine Learning Pipeline on AWS and Deep Learning on AWS, followed by around a year of hands-on model building, before attempting this course. It is an advanced course and moves at that pace.

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Certification

This course does not map to a single certification exam, but it builds skills directly relevant to AWS's machine learning certification path.

Note for anyone researching this course: the AWS Certified Machine Learning – Specialty exam (MLS-C01) that older course listings reference was retired on 31 March 2026. Existing certifications remain valid for three years from the date earned, but the exam can no longer be taken. The current associate-level successor is AWS Certified Machine Learning Engineer – Associate (MLA-C01), which validates the ability to build, operationalize, deploy, and maintain ML solutions and pipelines on AWS. The SageMaker Pipelines, Model Registry, Model Monitor, and deployment content in this course maps closely to what MLA-C01 tests.

📘 Amazon SageMaker Studio for Data Scientists Outline

Launch SageMaker Studio from the Service Catalog
Navigate the SageMaker Studio interface
Demo: SageMaker UI walkthrough
Demo: creating an EMR cluster in the SageMaker UI
Lab 1: setting up Amazon SageMaker Studio

Collect, clean, visualize, analyze, and transform data in SageMaker Studio
Set up a repeatable data processing process
Validate that collected data is ML-ready
Detect bias in collected data and estimate baseline model accuracy
Lab 2: analyze and prepare data using Amazon SageMaker Data Wrangler
Lab 3: analyze and prepare data at scale using Amazon EMR
Lab 4: data processing using SageMaker Processing and the SageMaker Python SDK
Lab 5: feature engineering using SageMaker Feature Store

Develop, tune, and evaluate models against business objectives, fairness, and explainability best practices
Fine-tune models using automatic hyperparameter optimization
Use Debugger to surface issues during development
Demos: algorithms in notebooks, debugging, and Autopilot
Lab 6: using SageMaker Experiments to track training and tuning iterations
Lab 7: analyzing, detecting, and setting alerts with SageMaker Debugger
Lab 8: using SageMaker Clarify for bias and explainability

Design and implement a deployment solution that meets inference requirements
Create, automate, and manage end-to-end ML workflows with SageMaker Pipelines
Use Model Registry to create model groups, register and manage versions, modify approval status, and deploy
Lab 9: inferencing with SageMaker Studio
Lab 10: using SageMaker Pipelines and Model Registry with SageMaker Studio

Configure Model Monitor to detect and alert on drift in data quality, model quality, bias, and feature attribution
Create a monitoring schedule with a predefined interval
Demo: model monitoring

Identify which resources accrue charges
Know when and how to shut down instances, notebooks, terminals, and kernels
Understand the SageMaker Studio update process

Bring together everything covered in the course by preparing, building, training, and deploying a model on a tabular dataset not used in earlier labs. Instructions are available at basic, intermediate, and advanced levels, so you can choose how much guidance you want.

❓ Frequently Asked Questions

No. This is an advanced course that assumes around a year of hands-on experience training, tuning, and deploying models, along with Python proficiency and familiarity with ML frameworks. It teaches you to work efficiently in SageMaker Studio, not to do machine learning. If you are earlier in that journey, build ML fundamentals first and come back.

MLA-C01, the AWS Certified Machine Learning Engineer – Associate exam, is the current successor and focuses on operationalizing and maintaining ML in production. If your interest is generative AI rather than classical ML, the relevant paths are AWS Certified AI Practitioner at foundational level and AWS Certified Generative AI Developer – Professional at professional level.

Most of it. There are ten labs across the three days plus a capstone project, alongside demonstrations and discussion. The final capstone has you build an end-to-end project on an unfamiliar tabular dataset, which is the closest the course gets to real work.

You prepare, build, train, and deploy a model on a tabular dataset not used in the earlier labs, choosing between basic, intermediate, and advanced instructions. The advanced path gives you far less scaffolding, which is worth attempting if you want to know whether the skills have really landed.

Completely different discipline. This course is about the classical ML lifecycle: preparing data, training and tuning your own models, and operating them. The generative AI courses are about building applications on top of existing foundation models. Many teams need both, but they are separate skill sets and separate audiences.

Yes. Private delivery works well here because the labs and capstone discussion can be oriented around the kinds of datasets and deployment constraints your team actually deals with.